Showing posts sorted by relevance for query xilinx. Sort by date Show all posts
Showing posts sorted by relevance for query xilinx. Sort by date Show all posts

Friday, April 26, 2019

Having dated for a while, Xilinx gets serious and buys Solarflare to raise its networking game (XLNX)

The fact that Xilinx did not have a reflexive day-after bounce says the stock may not be out of the woods yet with a retest of this morning's $110.31 low being quite possible. However...this is a class act company and trying to get too cute on timing could leave one watching rather than riding.
$116.54 up $0.68 on a day with sometime competitor NVIDIA down $10.89 (-5.83%) and Obergruppenführer Intel down 10.57% (-$6.09) at $51.52


XLNX Xilinx, Inc. daily Stock Chart

Over there, on the right.

From The Register:
FPGA daddy Xilinx is buying California-based silicon design startup Solarflare Communications to improve its networking credentials.

Solarflare uses field-programmable gate arrays to build SmartNICs – network interface cards that run network, storage and compute acceleration using specialised on-board chips, eliminating the need to run these workloads on CPUs. This can simultaneously improve server performance and enable 10Gb Ethernet connectivity.

Solarflare also develops application acceleration software that helps customers take full advantage of the cards. Historically, the company's products have been aimed at the financial services industry, where minimising latency is paramount.

Solarflare is headquartered in Irvine, California, with R&D facilities in the US, UK and India.
Xilinx said the deal fits into its "data centre first" strategy, unveiled last year and intended to transform it from a chip vendor into a platform company – like Intel, Nvidia or Arm.
The financial details of the acquisition have not been disclosed.

Xilinx invented the first commercial FPGA back in 1985 and is considered a pioneer of the fabless semiconductor production model – since it has never owned any manufacturing facilities. The company counts Huawei, SK Telecom and Microsoft among its customers, with more than half of Azure servers reportedly containing some form of Xilinx wizardry.

Xilinx has been working with Solarflare since 2017 and participated in its latest funding round in 2018. At the Open Compute Summit in San Jose in March, the two companies demonstrated their first joint product – a single-chip, FPGA-based 100G SmartNIC, processing 100 million packets per second, both receiving and transmitting, while consuming less than 75W.

"The Solarflare team has worked very closely with Xilinx on next-generation networking technology and business collaboration since Xilinx became a strategic investor," said Russell Stern, head honcho at Solarflare....MORE
And a little more soberly, EE Times:
Xilinx to Buy Networking Technology Firm Solarflare 

Recently:
"Xilinx Earnings Miss Wall Street's Target, Stock Tanks Late" (XLNX)
UPDATED—Xilinx Releases Q4 and Year End Financials (XLNX)
Chips: Investor's Business Daily Is Still Giving Sweet, Sweet Love to Xilinx (XLNX)

Friday, March 22, 2019

Chips: Zacks Likes Xilinx (XLNX)

So do we.

XLNX Xilinx, Inc. daily Stock Chart

After hitting an all-time high yesterday ($130.57) the stock is falling back along with the wider market, down 58 cents at $129.47.

From Zacks:
Chipmakers on Fire: 3 Under-the-Radar Stocks to Win
***
...Xilinx
Xilinx, Inc. (XLNX - Free Report) designs and develops programmable devices and associated technologies. The San Jose, CA-based company has been progressing by leaps and bounds from healthy sales of chips for data centers as well as 5G wireless networks.
Xilinx has also seen solid demand for its other 5G products, including the one-chip combination of analog radio chips and digital processors. Tristan Gerra, a senior research analyst with RW Baird, added that “we think Xilinx will continue to see design win activity for 5G in multiple years ahead.”
Xilinx currently has a Zacks Rank #1 (Strong Buy). In the past 60 days, the company has seen nine earnings estimates move north, while none moved south for the current year. The Zacks Consensus Estimate for earnings rose 6.1% in the same period. 
The company’s expected earnings growth rate for the current year is 23.3%, more than the Semiconductors - Programmable Logic industry’s projected gain of 22.7%. The company has outperformed the broader industry so far this year (+52.7% vs +48.3%)....MORE
Also at Zacks, March 19, "Xilinx (XLNX) Gains As Market Dips: What You Should Know"

Previously on Climateer:
Feb 28
"Top-Rated Chipmaker Xilinx Gets Big Price-Target Hike On 5G Prospects" (XLNX; NVDA)
Although the stock is more extended than NVIDIA was at a similar stage of development, XLNX is clearly the new "It Girl" of the chip world. (sorry NVDA)...
... IBD has been cheerleading the stock for a while now and we have a few posts as well:
Feb 6
Watch Out NVIDIA: "Amazon, Huawei efforts show move to AI-centric chips continues"
This move toward specialized chips is something the cognoscenti have been talking about for a few years, see after the jump if interested....
Jan 29 
A Look At the Components Of the PHLX Semiconductor Index (SOX)
Jan. 24
Watch Out Nvidia, Xilinx Is Performing (reports, beats, pops) XLNX; NVDA
Xilinx with their field-programmable gate array approach versus Nvidia's more generalist chips is an example of the type of competition experts were predicting NVDA would be facing, some over two years ago, links after the jump...
Sept 2018 
Hot Chips 2018 Symposium on High Performance Chips
Sept 2018 
Chips: "A Rogues Gallery of Post-Moore’s Law Options"
Aug 18
Ahead of NVIDIA Earnings: The Last of the "Easy" Comparisons (NVDA)
Beating that Q3 2017 EPS, 90 cents, by double i.e. $1.80 or more, is doable but AI and data centers will have to pick up the slack from the Q1 and Q2 cryptocurrency bump that started declining with Bitmain and other miners use of ASICs rather than GPU's.

Going forward the trend toward specialist proprietary chips, see Tesla's development of their own chips etc, etc will leave NVIDIA with a couple holes in the potential addressable markets they will want to fill.

Additionally, the smaller pups, some still in stealth, are nipping at the big dog's heels, making it more expensive for NVIDIA to maintain their edge in architecture....
Aug 2018
Artificial Intelligence Chips: Past, Present and Future
May 2018
Chipmakers Battle To Power Artificial Intelligence In Cloud Data Centers" (AMD; NVDA; XLNX; INTC)
May 2018 
"Xilinx Analyst Day Plays Heavy on AI" (XLNX)
May 2018
"Intel vs. Nvidia: Future of AI Chips Still Evolving" (INTC; NVDA)
Dec. 2017
“'The Ultimate Trading Machine' from Penguin Computing sets Record for Low Latency" 

Wednesday, April 24, 2019

"Xilinx Earnings Miss Wall Street's Target, Stock Tanks Late" (XLNX)

There was an odd trade in very early afterhours action, maybe eight minutes after the bell where someone sold ~17,000 shares at the market and at a $1.79 discount to the previous trade.
Said someone wanted out and didn't want to go sit on the offer and I wondered why they were in such a hurry with the earnings not due for another 12 minutes.
Odd.

Today's last AH print was at  $127.8099  down $11.9101 (8.52%) with the after-hours low being $121.63.  (16:27:05 PM)
Anyway, all that is for others to ponder, on to the explanations.

First up, Investor's Business Daily:
Chipmaker Xilinx (XLNX) late Wednesday missed analyst estimates for earnings in its fiscal fourth quarter, but beat views on sales. The Xilinx earnings news pummeled its stock in extended trading.

The San Jose, Calif.-based company earned an adjusted 94 cents a share on sales of $828 million in the March period. Analysts expected Xilinx earnings of 96 cents a share on sales of $819 million, according to Zacks Investment Research. On a year-over-year basis, Xilinx earnings rose 34% while sales climbed 30%.

Xilinx stock tumbled 12%, near 123, in after-hours trading on the stock market today. During the regular session, Xilinx stock rose 1.9% to 139.72.

For the current quarter, Xilinx expects sales of $850 million, based on the midpoint of its guidance. It did not give a target for earnings per share....MORE
And the above-mentioned Zacks:
Xilinx (XLNX) Lags Q4 Earnings Estimates
Xilinx (XLNX - Free Report) came out with quarterly earnings of $0.94 per share, missing the Zacks Consensus Estimate of $0.96 per share. This compares to earnings of $0.64 per share a year ago. These figures are adjusted for non-recurring items.
This quarterly report represents an earnings surprise of -2.08%. A quarter ago, it was expected that this computer chipmaker would post earnings of $0.85 per share when it actually produced earnings of $0.92, delivering a surprise of 8.24%.

Over the last four quarters, the company has surpassed consensus EPS estimates three times....MORE
The company also announced they made an acquisition beefing up their data center offerings:
Xilinx to Acquire Solarflare 
But the press release has the same timestamp as the earnings release.  Don't do that. Get the numbers out first.
The stock was up 64.0% Year-to-Date; Dec. 31 to April 24 - close-to-close, but tomorrow's open is definitely going to cut into the YTD and annualized returns.

If we open at that $127.81 we're back to April 4th when it closed at $127.82.

XLNX Xilinx, Inc. daily Stock Chart

It's going higher but probably not tomorrow.

Tuesday, February 26, 2019

"Top-Rated Chipmaker Xilinx Gets Big Price-Target Hike On 5G Prospects" (XLNX; NVDA)

Although the stock is more extended than NVIDIA was at a similar stage of development, XLNX is clearly the new "It Girl" of the chip world. (sorry NVDA):

https://www.finviz.com/chart.ashx?t=XLNX&ty=c&ta=1&p=d&s=l
This is part of the trend away form generalist chips such as NVIDIA's toward more targeted approaches, in this case field programmable gate arrays which as the name implies can be fine-tuned by the end user. We saw a similar specialization begin a couple years ago in the cryptocurrency mining rigs when they moved to less versatile ASIC's versus AMD and NVIDIA graphics processors.
From Investor's Business Daily:
A Wall Street brokerage on Tuesday raised its price target on Xilinx (XLNX), saying the chipmaker is poised to benefit in a big way from the transition to 5G wireless. However, the report didn't help Xilinx stock, which fell on Tuesday.

Despite all the hype about fifth-generation wireless technology, consumer demand is an open question, KeyBanc Capital Markets said in a note to clients. So, the best way for investors to play the 5G transition is to bet on companies building the network infrastructure.

"At this point, we believe the most compelling way to benefit from the 5G upgrade cycle is on the infrastructure side, for which we recommend investors buy XLNX," KeyBanc said. It said Xilinx has "outsized positioning in 5G."

KeyBanc analyst John Vinh reiterated his overweight rating on Xilinx stock and raised his price target to 140 from 110.

Xilinx Stock Retreats After Hitting Record High
Xilinx stock dropped 1% to 122.78 on the stock market today. On Monday, Xilinx stock notched a record high of 126.29. Xilinx currently ranks No. 35 on the IBD 50 list of top-performing growth stocks.

At MWC Barcelona, formerly Mobile World Congress, smartphone vendors showed off their first 5G handsets. Samsung, LG, Huawei and others unveiled 5G smartphones at the trade show this week....MORE
IBD has been cheerleading the stock for a while now and we have a few posts as well:
Feb 6
Watch Out NVIDIA: "Amazon, Huawei efforts show move to AI-centric chips continues"
This move toward specialized chips is something the cognoscenti have been talking about for a few years, see after the jump if interested....
Jan 29 
A Look At the Components Of the PHLX Semiconductor Index (SOX)
Jan. 24
Watch Out Nvidia, Xilinx Is Performing (reports, beats, pops) XLNX; NVDA
Xilinx with their field-programmable gate array approach versus Nvidia's more generalist chips is an example of the type of competition experts were predicting NVDA would be facing, some over two years ago, links after the jump...
Sept 2018 
Hot Chips 2018 Symposium on High Performance Chips
Sept 2018 
Chips: "A Rogues Gallery of Post-Moore’s Law Options"
Aug 18
Ahead of NVIDIA Earnings: The Last of the "Easy" Comparisons (NVDA)
Beating that Q3 2017 EPS, 90 cents, by double i.e. $1.80 or more, is doable but AI and data centers will have to pick up the slack from the Q1 and Q2 cryptocurrency bump that started declining with Bitmain and other miners use of ASICs rather than GPU's.

Going forward the trend toward specialist proprietary chips, see Tesla's development of their own chips etc, etc will leave NVIDIA with a couple holes in the potential addressable markets they will want to fill.

Additionally, the smaller pups, some still in stealth, are nipping at the big dog's heels, making it more expensive for NVIDIA to maintain their edge in architecture....
Aug 2018
Artificial Intelligence Chips: Past, Present and Future
May 2018
Chipmakers Battle To Power Artificial Intelligence In Cloud Data Centers" (AMD; NVDA; XLNX; INTC)
May 2018 
"Xilinx Analyst Day Plays Heavy on AI" (XLNX)
May 2018
"Intel vs. Nvidia: Future of AI Chips Still Evolving" (INTC; NVDA)
Dec. 2017
“'The Ultimate Trading Machine' from Penguin Computing sets Record for Low Latency" 

Thursday, January 24, 2019

Watch Out Nvidia, Xilinx Is Performing (reports, beats, pops) XLNX; NVDA

Xilinx with their field-programmable gate array approach versus Nvidia's more generalist chips is an example of the type of competition experts were predicting NVDA would be facing, some over two years ago, links after the jump.
In pre-market trade XLNX is up $7.98 (8.91%) at $97.53. The last after-hours print yesterday had the stock up 9.99% so the enthusiasm is a bit less, ah, enthusiastic but enough to grab the attention os some sharp-eyed algos.
Speaking of enthusiastic, IBD seems pretty fired up.

From Investor's Business Daily:
Dow Jones Futures: Xilinx Set To Blast Into Buy Zone As Chip Stocks Soar On Earnings 
7:26 AM ET
Dow Jones futures rose modestly early Thursday, along with S&P 500 futures. Nasdaq futures fared a little better, after chip stocks Xilinx (XLNX), Lam Research (LRCX) and Texas Instruments (TXN) reported earnings late. Top-rated Xilinx stock soared, signaling a breakout, while Lam Research stock and Texas Instruments stock also rose. But those results also lifted other chip stocks, including Intel (INTC), Advanced Micro Devices (AMD), Applied Materials (AMAT) and Nvidia (NVDA) — ahead of Intel earnings Thursday night. That's a good sign for the stock market rally, as semiconductors usually participate in successful uptrends....

...Xilinx Earnings Crush Views; Xilinx Stock Signals Breakout
Xilinx earnings soared 42% in the fiscal third quarter. Revenue leapt 34%, the fourth straight quarter of accelerating growth. The chipmaker also gave upbeat Q4 revenue guidance. Xilinx stock, a member of the IBD 50 and on the Leaderboard watch list, shot up 10% to 98.80 early Thursday. That suggests Xilinx stock will clear a 95.28 cup base buy point at the market open....
...MORE

And from the company:
Xilinx Reports Record Revenues And EPS In Fiscal Third Quarter

Previously on the state of play:
Dec 2018
A Dip Into Chips: "AI Chip Architectures Race To The Edge"
Sept 2018 
Hot Chips 2018 Symposium on High Performance Chips
Sept 2018 
Chips: "A Rogues Gallery of Post-Moore’s Law Options"
May 2018
Chipmakers Battle To Power Artificial Intelligence In Cloud Data Centers" (AMD; NVDA; XLNX; INTC)
May 2018 
"Xilinx Analyst Day Plays Heavy on AI" (XLNX)
May 2018
"Intel vs. Nvidia: Future of AI Chips Still Evolving" (INTC; NVDA)
Dec. 2017
“'The Ultimate Trading Machine' from Penguin Computing sets Record for Low Latency"
Oct. 2017
"The Natural Evolution of Artificial Intelligence" 

Tuesday, April 23, 2019

Chips: Investor's Business Daily Is Still Giving Sweet, Sweet Love to Xilinx (XLNX)

I miss Chef.
The stock is up $1.74 (+1.29%) at $136.18, just under the $137.23 all-time high on April 17:

XLNX Xilinx, Inc. daily Stock Chart
From IBD, April 22:

5G Wireless Business Has 'Room To Grow' For This Hot Chip Stock 
Investment bank Morgan Stanley on Monday raised its price target on Xilinx stock ahead of the highflying chipmaker's quarterly earnings report this week.
Morgan Stanley analyst Joseph Moore reiterated his overweight rating on Xilinx (XLNX), but raised his price target to 137 from 101.

Xilinx stock dipped 0.4% to 134.44 on the stock market today. It broke out of a seven-week consolidation period at a buy point of 95.28 on Jan. 24.

Xilinx's burgeoning business selling chips for the buildout of 5G wireless networks "has room to grow," Moore said in a note to clients. 5G is an abbreviation for fifth-generation cellular networks....
...MORE

Previously:
Chips: Zacks Likes Xilinx (XLNX)
So do we....

"Top-Rated Chipmaker Xilinx Gets Big Price-Target Hike On 5G Prospects" (XLNX; NVDA)
Although the stock is more extended than NVIDIA was at a similar stage of development, XLNX is clearly the new "It Girl" of the chip world. (sorry NVDA)...
... IBD has been cheerleading the stock for a while now and we have a few posts as well:
Watch Out NVIDIA: "Amazon, Huawei efforts show move to AI-centric chips continues"
This move toward specialized chips is something the cognoscenti have been talking about for a few years, see after the jump if interested....
Chips: The Accelerator Wall—A New Problem for a Post-Moore’s Law World (GPU; ASIC; FPGA)

Watch Out Nvidia, Xilinx Is Performing (reports, beats, pops) XLNX; NVDA
Xilinx with their field-programmable gate array approach versus Nvidia's more generalist chips is an example of the type of competition experts were predicting NVDA would be facing, some over two years ago, links after the jump...
Artificial Intelligence Chips: Past, Present and Future
Chipmakers Battle To Power Artificial Intelligence In Cloud Data Centers" (AMD; NVDA; XLNX; INTC)
"Xilinx Analyst Day Plays Heavy on AI" (XLNX)

A Dip Into Chips: "AI Chip Architectures Race To The Edge"
"Why Alibaba is betting big on AI chips and quantum computing"
Hot Chips 2018 Symposium on High Performance Chips
Chips: "A Rogues Gallery of Post-Moore’s Law Options"

Monday, May 20, 2019

Chips: "Xilinx refines AI chips strategy: It’s not just the neural network"

The author of this piece, , used to run Barron's 'Tech Trader' and 'Tech Trader Daily' columns. When he assumed those chores after Eric Savitz left we greeted him and wished him well: "Barron's and Journo Tiernan Ray are Class Acts " but he had some gargantuan shoes to fill and I wasn't sure Barron's would remain one of our sources for tech.

And then someone pointed out that Mr. Ray was getting answers out of NVIDIA's Jenson Huang that were head and shoulders above anything the NVDA CEO would tell anyone else and I started watching for it and son-of-a-gun it was like being a fly on the wall. Tiernan knows this stuff and the tech guys recognize it.

Here he is at ZD Net, May 15, 2019 
Xilinx hopes to take a big chunk of the market for semiconductors that process machine learning inference tasks by convincing developers it's not only about the neural network performance. It's about the entire application. 
Chip maker Xilinx on Tuesday held its annual "analyst day" event in New York, where it told Wall Street's bean counters what to expect from the stock. During the event the company fleshed out a little more how it will go after a vibrant market for data center chips, especially those for machine learning.

That market is expected to rise to $6.1 billion by 2024 from $1.8 billion in 2020. 
The focus for Xilinx is a raft of new platform products that take its capabilities beyond the so-called field-programmable gate arrays, or FPGAs, that it has sold for decades. That requires selling developers of AI applications on the notion there's more than just the neural network itself that needs to be sped up in computers.

Data center is a small part of Xilinx's overall revenue, at $232 million in the fiscal year ended in March, out of a total of $3.1 billion in company revenue. However, it is the fastest-growing part of the company, rising 46% last year. The company yesterday said data center revenue growth is expected to accelerate, rising in a range of 55% to 65% this fiscal year, versus the compounded annual growth of 42% in the period 2017 through 2019.  
https://zdnet1.cbsistatic.com/hub/i/2019/05/15/b0611a1f-32ea-4ef8-ac94-d98039155756/ee02c3b2e4cbb12e918ff0a984831960/xilinx-versal-ai-engine-tile.png
Xilinx expects to gain ground in machine learning inference by virtue of "tiles," compute blocks that connect to one 
another over a high-speed memory bus, inside the "AI Engines" portion of its "Versal" programmable chips.
To do so, Xilinx is moving past its heritage in FPGAs, to something more complex. FPGAs contain a kind of vast field of logic gates that can be re-arranged, so they have the prospect of being tuned to a task and therefore being of higher performance and greater energy efficiency.
...MUCH MORE

Also by Mr. Ray, commentary on the AI company co-founded by Eon Musk:
OpenAI has an inane text bot, and I still have a writing job

Sunday, October 1, 2017

"The Natural Evolution of Artificial Intelligence"

From Barron's Tech Trader, Sept. 24:
As everyday items get “smart,” the technology around artificial intelligence gets more real. The rising stars in this drama include Xilinx, Synopsys, and Cadence Design.

One thing that often fuels technological innovation is the ton of money that gets thrown around.

Case in point: Japanese conglomerate SoftBank Group’s purchase last year of ARM Holdings, a British computer-chip company, for $32 billion in cash. That deal, which came at an astounding premium of 43% above ARM’s value at the time, has no doubt fueled the animal spirits of tech to dream up more companies that can be taken out.

Masayoshi Son, the 60-year-old chairman of SoftBank (ticker: 9984.Japan) who delights in stirring up a commotion, joked at the time that he spends like crazy because he “still feels young.” This past February, Son said, in no way jokingly, that the real reason he’s in such a hurry to spend is that 2018 is the year artificial intelligence surpasses human intelligence, and he wants to control the technology when the machines take over.

Whether from youthful exuberance, or fears of mortality, Son and others like him will most likely continue to fund anything that smells of artificial intelligence, the tech topic du jour. The market is already seeing waves of AI companies, and the chip industry is at the epicenter.

Nearly two years ago, Barron’s wrote that the rise of cloud computing—and with it, aspects of artificial intelligence such as “machine learning”—would spark a wholesale shift in the nature of computer chips (see “Watch Out Intel, Here Comes Facebook,” Oct. 31, 2015).

SOME THINGS WE PREDICTED have played out. Mobileye, a company we featured, got bought by Intel (INTC) this year for $13.7 billion. Two other chip makers we pointed to as potential winners, Nvidia (NVDA) and Advanced Micro Devices (AMD), have seen their shares soar since the article, while shares of incumbent chip giant Intel have lagged, as we predicted.

The market has come to appreciate that Nvidia’s sales of so-called graphics processing chips, or GPUs, are central to the expansion of AI computing by Alphabet’s (GOOGL) Google, Amazon.com (AMZN), and many other giants.

Advanced Micro Devices took on new relevance last week with the rumor that it may be selling chips to Tesla (TSLA) to help with autonomous-car functions. Tesla is already a customer of Nvidia’s. Whether true or not, the speculation stirred some positive words on Wall Street about AMD’s AI potential.

One topic we didn’t pay as much attention to, but that’s starting to get some AI-shine, concerns programmable chips sold by Xilinx (XLNX). Xilinx competes with Intel, which two years ago bought Xilinx’s main competitor, Altera, for $16.7 billion. The third name sometimes lumped in with Xilinx and Altera is Lattice Semiconductor (LSCC), whose sale to a Chinese investment firm was blocked last week by the administration of Donald Trump on grounds of national security.

Xilinx’s parts, called “field-programmable gate arrays,” or FPGAs, aren’t yet synonymous with AI like those of Nvidia. But it has been selling more and more parts to Amazon for its Amazon Web Services. When chips start getting used in cloud computing, it’s predictable they soon get the imprimatur of AI as a side effect.

Xilinx shares have trailed the market this year, rising just 14.8% compared to the 19.4% rise in the Nasdaq Composite Index. That’s despite the fact that its profile as a cloud supplier has grown, and despite its being an oft-mentioned acquisition target. Its shares trade at 25 times next year’s projected earnings per share. That is reasonable for an earnings growth rate of just 10.5%, and cheap compared to the multiple of almost 37 times ARM Holdings got from SoftBank.

Other companies that could get swept up in the fervor include Synopsys (SNPS), a company we profiled in that 2015 story, and Cadence Design Systems (CDNS). The two make software used to design chips. In a world of chips customized for artificial-intelligence tasks, it makes sense that the tools to make those chips will play an important role.

Synopsys founder and CEO Aart de Geus, one of the industry’s deep thinkers, told Barron’s that the next big thing that will propel chip development is when all objects become “smart.” He considers that a better term than AI, given all the misconceptions and stigmas attached to AI.

Synopsys and Cadence may have an opportunity to help with the design of all those custom chips that will increasingly vie with the chips and other parts made by Nvidia, AMD, and Xilinx....MORE

Wednesday, April 24, 2019

UPDATED—Xilinx Releases Q4 and Year End Financials (XLNX)

UPDATE II "Xilinx Earnings Miss Wall Street's Target, Stock Tanks Late" (XLNX)

UPDATE I: No longer muted  down $16.39 (-11.73%) at $123.33

Original post
Initial response from the stock: muted.  Up $1.12 (0.8%) at $140.84.
More to come.

From the company:

Xilinx Reports Record Revenues Exceeding $3 Billion For Fiscal 2019
Fiscal fourth quarter revenues up 30% year over year
Apr 24, 2019, 16:20 ET
SAN JOSE, Calif., April 24, 2019 /PRNewswire/ -- Xilinx, Inc. (Nasdaq: XLNX) today announced record revenues of $3.06 billion for fiscal year 2019, up 24% from the prior fiscal year. Revenues were $828 million for the fourth quarter of fiscal year 2019, up 4% from the prior quarter and up 30% year over year.

GAAP net income for fiscal year 2019 was $890 million, or $3.47 per diluted share.  Non-GAAP net income for fiscal year 2019 was $892 million, or $3.48 per diluted share. GAAP net income for the March quarter was $245 million, or $0.95 per diluted share.  Non-GAAP net income for the March quarter was $242 million, or $0.94 per diluted share.

The Xilinx Board of Directors declared a quarterly cash dividend of $0.37 per outstanding share of common stock payable on June 3, 2019 to all stockholders of record at the close of business on May 16, 2019.

Additional fourth quarter of fiscal year 2019 comparisons are provided in the charts below. Due to the adoption of the new revenue recognition standard in the first quarter of fiscal year 2019, all 2018 results have been restated to conform with the new standard.

Q4 2019 Financial Highlights

(In millions, except EPS)     





GAAP












Q4
Q3
Q4






FY 2019
FY 2019
FY 2018

Q-T-Q
Y-T-Y

Net Revenues*
$828
$800
$638

4%
30%

Operating income
$250
$258
$163

-3%
53%

Net income
$245
$239
$145

2%
68%

Diluted earnings per share
$0.95
$0.93
$0.56

2%
70%












Non-GAAP












Q4
Q3
Q4






FY 2019
FY 2019
FY 2018

Q-T-Q
Y-T-Y

Net Revenues*
$828
$800
$638

4%
30%

Operating income
$259
$263
$197

-2%
31%

Net income
$242
$237
$181

2%
34%

Diluted earnings per share
$0.94
$0.92
$0.70

2%
34%


* No adjustment between GAAP and Non-GAAP
"Fiscal year 2019 was truly an exceptional year for Xilinx. For the year, we exceeded $3 billion in annual revenues for the first time and posted 24% growth from last year, driven by Advanced Product revenues which grew 40% year over year.  In addition, we demonstrated strong profitability by posting over 30% growth in both non-GAAP operating income and non-GAAP diluted earnings per share. We are executing to our strategy and focusing on growth across our portfolio as we continue our transformation to a platform company," said Victor Peng, president and chief executive officer, Xilinx....
...MORE

Conference call information:


Friday, February 7, 2020

Chips: AI Deep Learning and More, The New Chip Bestiary

From DataCenterDynamics, January 24:

With AI workloads set to dominate the future, there's an uncertainty around the hardware that’s aiming to dethrone the GPU
In 1971, Intel, then a manufacturer of random access memory, officially released the 4004, its first single-chip central processing unit, thus kickstarting nearly 50 years of CPU dominance in computing.

In 1989, while working at CERN, Tim Berners-Lee used a NeXT computer, designed around the Motorola 68030 CPU, to launch the first website, making the machine used the world’s first web server.

CPUs were the most expensive, the most scientifically advanced, and the most power-hungry parts of a typical server: they became the beating hearts of the digital age, and semiconductors turned into the benchmark for our species' advancement.
This feature appeared in the January issue of DCD Magazine. Subscribe for free today.
Intel's domination
Few might know about the Shannon limit or Landauer's principle, but everyone knows about the existence of Moore’s Law, even if they have never seated a processor in their life. CPUs have entered popular culture and, today, Intel rules this market, with a near-monopoly supported by its massive R&D budgets and extensive fabrication facilities, better known as ‘fabs.’
But in the past two or three years, something strange has been happening: data centers started housing more and more processors that weren’t CPUs.

It began with the arrival of GPUs. It turned out that these massively parallel processors weren’t just useful for rendering video games and mining magical coins, but also for training machines to learn - and chipmakers grabbed onto this new revenue stream for dear life.

Back in August, Nvidia’s CEO Jen-Hsun ‘Jensen’ Huang called AI technologies the “single most powerful force of our time.” During the earnings call, he noted that there were currently more than 4,000 AI start-ups around the world. He also touted examples of enterprise apps that could take weeks to run on CPUs, but just hours on GPUs.

A handful of silicon designers looked at the success of GPUs as they were flying off the shelves, and thought: we can do better. Like Xilinx, a venerable specialist in programming logic devices. The granddaddy of custom silicon, it is credited with inventing the first field-programmable gate arrays (FPGAs) back in 1985.
Applications for FPGAs range from telecoms to medical imaging, hardware emulation, and of course, machine learning workloads. But Xilinx wasn’t happy with adopting old chips for new use cases, the way Nvidia had done, and in 2018, it announced the adaptive compute acceleration platform (ACAP) - a brand new chip architecture designed specifically for AI.

“Data centers are one of several markets being disrupted,” CEO Victor Peng said in a keynote at the recent Xilinx Developer Forum in Amsterdam. “We all hear about the fact that there's zettabytes of data being generated every single month, most of them unstructured. And it takes a tremendous amount of compute capability to process all that data. And on the other side of things, you have challenges like the end of Moore's Law, and power being a problem.

"Because of all these reasons, John Hennessy and Dave Patterson - two icons in the computer science world - both recently stated that we were entering a new golden age of architectural development."
He continued: “Simply put, the traditional architecture that’s been carrying the industry for the last 40 to 50 years is totally inadequate for the level of data generation and data processing that’s needed today.”

“It is important to remember that it’s really, really early in AI,” Peng later told DCD. “There’s a growing feeling that convolutional and deep neural networks aren’t the right approach. This whole black box thing - where you don’t know what’s going on and you can get wildly wrong results, is a little disconcerting for folks.”

A new approach
Salil Raje, head of the Xilinx data center group, warned: “If you’re betting on old hardware and software, you are going to have wasted cycles. You want to use our adaptability and map your requirements to it right now, and then longevity. When you’re doing ASICs, you’re making a big bet.”
Another company making waves is British chip designer Graphcore, quickly becoming one of the most exciting hardware start-ups of the moment.

Graphcore’s GC2 IPU has the world’s highest transistor count for a device that’s actually shipping to customers - 23,600,000,000 of them. That’s not nearly enough to keep up with the demands of Moore’s Law - but it’s a whole lot more transistor gates than in Nvidia’s V100 GPU, or AMD’s monstrous 32-core Epyc CPU.

“The honest truth is, people don’t know what sort of hardware they are going to need for AI in the near future,” Nigel Toon, the CEO of Graphcore, told us in August. “It’s not like building chips for a mature technology challenge. If you know the challenge, you just have to engineer better than other people.

“The workload is very different, neural networks and other structures of interest change from year to year. That’s why we have a research group, it’s sort of a long-distance radar.

"There are several massive technology shifts. One is AI as a workload - we’re not writing programs to tell a machine what to do anymore, we’re writing programs that tell a machine how to learn, and then the machine learns from data. So your programming has gone kind of ‘meta.’ We’re even having arguments across the industry about the way to represent numbers in computers. That hasn’t happened since 1980....
....MUCH MORE 

Friday, January 25, 2019

IBD: "Dow Jones Rises 3,100 Points From Dec. 26 Low; 6 Top Stocks Break Out"

Yeah, yeah. We've already thanked Santa and cut the risk profile, letting-go of the triple-leveraged index stuff and figuring basis on the S&P e-mini options. On to the individual issues and maybe a sinkhole of a Broadway production.

From Investor's Business Daily:
The Dow Jones industrial average rose 0.8% and joined a broad and sharp rally in stocks today after President Donald Trump agreed to halt a one-month partial government shutdown. At Friday's session high of 24,860, the 30-stock blue chip index has now bungee-jumped 3,148 points, or more than 14%, from its Dec. 26 intraday low.

Meanwhile, BioTelemetry (BEAT), Domino's Pizza (DPZ), Tencent Music Entertainment (TME), PayPal (PYPL), Xilinx (XLNX) and Cornerstone OnDemand (CSOD) showed the type of weekly price gains shown by true market leaders.
Domino's Pizza, Tencent Music, PayPal and Xilinx are current members of IBD Leaderboard. Since BioTelemetry ranks within the top 10 inside the IBD 50, the stock's daily and weekly charts are annotated in real time to note buy points, sell signals and important aspects of price-and-volume action.

Top Stocks Do This
Xilinx, the specialist in programmable chips for data center and communications markets, blasted more than 17% higher for the week, leaving a 95.28 buy point in a cup without handle in the dust. Cornerstone rose more than 2% on Friday and rolled past a 55.54 proper entry in a base that features two major sell-offs typically seen in a good double-bottom base.

The Nasdaq composite led Friday's rally with a 1.3% gain. It also marked a fourth up week in a row, rising fractionally for the week. For the week, the S&P 500 lost about 0.3% despite Friday's 0.8% lift. The S&P SmallCap 600 snapped a four-week win streak, but gave back less than 0.2% for the week.
The iShares PHLX Semiconductor (SOXX) ETF gained more than 4% for the week. It's now testing upside resistance near the 40-week moving average....MUCH MORE
Recently:
Jan 20
Equities: Dear Santa,
***
***
If you look at the last five candlesticks, Friday Jan. 18 through Friday Jan. 25 (Monday holiday, no candle) we are pretty much sideways. Many more earnings next week to direct us into break-up or break-down.
Sounds like mental illness.

Jan. 24
Watch Out Nvidia, Xilinx Is Performing (reports, beats, pops) XLNX; NVDA
Nvidia CEO Huang explains how AI Changes Everything (NVDA)
Jan 25
"Party is over for dirt-cheap solar panels, says China exec"

Monday, September 3, 2018

Chips: "A Rogues Gallery of Post-Moore’s Law Options"

From The Next Platform:
The last decade, and few years in particular, have brought a bevy of new architectures to bear for a market keen to understand what comes after Moore’s Law. From established technologies like neuromorphic and quantum devices to more recent deep learning, graph, and memory processors there is no question there are new options, but what is not clear is how to evaluate them against each other or traditional devices.

This problem is at the heart of a recent project within Georgia Tech’s Center for Research into Novel Computing Hierarchies. In 2017, researchers from the group created a testbed for emerging architectures humorously called the “Rogue’s Gallery” of chips and systems that are off the map in terms of past architectural trends. The goal is to evaluate new architectures with an eye on how new designs might penetrate the market and specifically how networking, scheduling, tooling, and other aspects will work.

The Rogues Gallery makes cutting-edge hardware available to a wide variety of researchers and application developers. Examples of such cutting-edge hardware include Emu Technology’s Chick, FPGA-based memory-centric platforms, Field Programmable Analog Devices (FPAAs), and others. Even companies like Intel and IBM are investigating novel hardware, Loihi and TrueNorth respectively.
Accelerators like GPUs have created a pronounced shift in HPC and machine learning, but there is a wide variety of possible architectural choices for the post-Moore era, including memory-centric, neuromorphic, quantum, and reversible computing. These revolutionary research fields combined with alternative materials-based approaches to silicon-based hardware have given us a bewildering array of options and “rogue devices” for the coming post-Moore era but little guidance on how to evaluate potential hardware for tomorrow’s application needs.
A testbed with novel architectures sounds like fun but there are some real challenges, particularly in budget-constrained research computing. One of the finer balancing acts the team has to consider is how to invest in understand new “rogues” without overcommitting resources since not everything they evaluate will be adopted by the market. Using new hardware via containers or cloud is a key part of keeping costs low although some vendors have contributed their hardware to the cause.

As the team notes, “not all rogues become long-term products. Some fade away within a few years (or be acquired by companies that fail to productize the technology). The overall infrastructure of a testbed focused on rogues must minimize up-front investment to limit the cost of “just trying it out” with new technology. As these early-access and prototype platforms change, the infrastructure must also adapt.” Finding the limits for technologies is part of the mission and as one might imagine, that insight from Georgia Tech researchers will be of great value to startup vendors in particular.
On hand at the Rogue’s Gallery is the EMU Chick, a desktop tower implementation of the Emu architecture. The Emu design focuses on migratory threads and memory side-processing architecture combined with a high-speed Rapid IO network. It comes with EMU build VM for compiling and simulating code. The EMU Chick has 8 EMU “nodes” and EMU compiler and simulator tools
Not all of the hardware the team explores is strictly experimental. For instance, current evaluations include:
  • Nallatech 385-A – Arria 10 board available for High-level synthesis with OpenCL
  • Nallatech 385-SoC– Arria 10 board that supports HDL and embedded ARM core
  • Intel Arria10 DevKit
  • Coming Soon: Nallatech 520N (Stratix 10)
  • Xilinx MpSOC board
  • Micron EX700 with AC-510 HMC + FPGA module (sponsored in part by Micron donation)
Tools: Intel FPGA SDK 2017 (17.1), Xilinx Vivado 16.3 and Xilinx SDAccel

...MORE

And more chips tomorrow,

Friday, May 25, 2018

"Xilinx Analyst Day Plays Heavy on AI" (XLNX)

Everybody wants to get into the AI/data center/chip, act.

From Tiernan Ray at Barron's Tech Trader Daily, May 23:

Monday, March 25, 2019

Chips: The Accelerator Wall—A New Problem for a Post-Moore’s Law World (GPU; ASIC; FPGA)

As grandmother used to say, if it's not one tham ding it's another.
From the brainiacs at IEEE Spectrum:

Specialized chips and circuits may not save the computer industry after all
Accelerators are already everywhere: The world’s Bitcoin is mined by chips designed to speed the cryptocurrency’s key algorithm, nearly every digital something that makes a sound uses hardwired audio decoders, and dozens of startups are chasing speedy silicon that could make deep learning AI omnipresent. This kind of specialization, where common algorithms once run as software on CPUs are made faster by recreating them in hardware, has been thought of as a way to keep computing from stagnating after Moore’s Law peters out in one or two more chip generations.

But it won’t work. At least, it won’t work for very long. That’s the conclusion that Princeton University associate professor of electrical engineering David Wentzlaff and his doctoral student Adi Fuchs come to in research to be presented at the IEEE International Symposium on High-Performance Computer Architecture this month. Chip specialization, they calculate, can’t produce the kinds of gains that Moore’s Law could. Progress on accelerators, in other words, will hit a wall just like shrinking transistors will, and it will happen sooner than expected.

To prove their point, Fuchs and Wentzlaff had to figure out how much of recent performance gains comes from chip specialization and how much comes from Moore’s Law. That meant examining more than 1,000 chip data sheets and teasing out what part of their improvement from generation to generation was due to better algorithms and their clever implementation as circuits. In other words, they were looking to quantify human ingenuity.

So they did what engineers do: They made it into a dimensionless quantity. Chip specialization return, as they called it, answers the question: “How much did a chip’s compute capabilities improve under a fixed physical budget” of transistors?

Using this metric, they evaluated video decoding on an application specific integrated circuit (ASIC), gaming frame rate on a GPU, convolutional neural networks on an FPGA, and Bitcoin mining on an ASIC. The results were not heartening: Gains in specialized chips are greatly dependent on there continuing to be more and better transistors available per square millimeter of silicon. In other words, without Moore’s Law, chip specialization’s powers are limited....MORE
Previously on specialized chips:
Watch Out NVIDIA: "Amazon, Huawei efforts show move to AI-centric chips continues"
Jan. 24
Watch Out Nvidia, Xilinx Is Performing (reports, beats, pops) XLNX; NVDA
Xilinx with their field-programmable gate array approach versus Nvidia's more generalist chips is an example of the type of competition experts were predicting NVDA would be facing, some over two years ago, links after the jump...
Dec 2018
A Dip Into Chips: "AI Chip Architectures Race To The Edge"
Sept. 2018
"Why Alibaba is betting big on AI chips and quantum computing"
Sept 2018 
Hot Chips 2018 Symposium on High Performance Chips
Sept 2018 
Chips: "A Rogues Gallery of Post-Moore’s Law Options"
Aug 18
Ahead of NVIDIA Earnings: The Last of the "Easy" Comparisons (NVDA)
Beating that Q3 2017 EPS, 90 cents, by double i.e. $1.80 or more, is doable but AI and data centers will have to pick up the slack from the Q1 and Q2 cryptocurrency bump that started declining with Bitmain and other miners use of ASICs rather than GPU's.

Going forward the trend toward specialist proprietary chips, see Tesla's development of their own chips etc, etc will leave NVIDIA with a couple holes in the potential addressable markets they will want to fill.

Additionally, the smaller pups, some still in stealth, are nipping at the big dog's heels, making it more expensive for NVIDIA to maintain their edge in architecture....
Aug 2018
Artificial Intelligence Chips: Past, Present and Future
May 2018
Chipmakers Battle To Power Artificial Intelligence In Cloud Data Centers" (AMD; NVDA; XLNX; INTC)
May 2018 
"Xilinx Analyst Day Plays Heavy on AI" (XLNX)
May 2018
"Intel vs. Nvidia: Future of AI Chips Still Evolving" (INTC; NVDA)
Dec. 2017
“'The Ultimate Trading Machine' from Penguin Computing sets Record for Low Latency"
Oct. 2017
"The Natural Evolution of Artificial Intelligence" 

"Nvidia CEO is 'more than happy to help' if Tesla's A.I. chip doesn't pan out" (NVDA; TSLA)

And April 2017
We've said NVIDIA probably has a couple year head start but this bears watching, so to speak....
***
The only reason for Tesla to do this is that NVIDIA's chips are general purpose whereas specialized chips are making inroads in stuff like crypto mining (ASICs), Google's Tensor Processing Units (TPUs) for machine learning and Facebook's hardware efforts.

In May, 2018 we met a person who knows about this stuff:

 AI VC: "We Are Here To Create" 
Sometimes the competition is just plain intimidating/scary/resistance-is-futile, smart.

June 2016 
Machine Learning: JP Morgan Compares Google's New Chip With NVIDIA's (GOOG; NVDA)

And many more, use the "search blog" box, upper left if interested.

Monday, April 22, 2019

Graphcore CEO Takes A Shot at NVIDIA, Talks His Book, Makes a Good Point (NVDA)

In November 2016 we headlined a post Artificial Intelligence: What Could Derail NVIDIA? A Lab in Shenzhen; A Basement in Moscow; An Office in Bristol (NVDA).
Bristol?...

Graphcore was the "Office in Bristol".
A year later: "Sequoia Backs Graphcore as the Future of Artificial Intelligence Processors" (NVDA; INTC):
Huh.
Sometimes you get lucky...


From EE Times:

GPUs Holding Back AI Innovation
GPUs are widely used to accelerate AI computing, but are the limitations of GPU technology slowing down innovation in the development of neural networks?
In a recent interview with EETimes (Graphcore CEO Touts 'Most Complex Processor' Ever), Nigel Toon, CEO of Graphcore, explained that while GPUs are good at running convolutional neural networks (CNNs), they are not suitable for running the more complex types of neural network needed for reinforcement learning and other futuristic techniques.

“A GPU is a pretty good solution — if all you’re doing is basic, feed-forward CNNs. The problem comes when you start to have more complex neural networks. Rather than just doing it a layer at a time, I want to be able to go through some layers then feed back, and I want to be able to store information on the side which I can use as context information as I look at the next data. And if my data is changing — so, rather than it being millions of static images that I can feed in in parallel — if it's video and I'm interested in sequential frames, it's much harder to feed that in in parallel and take advantage of the wide SIMD paths in a GPU,” he said.

Visionary Approval
As part of our longer conversation, Toon noted that Graphcore has captured the interest of such AI visionaries as Demis Hassabis, a founder of DeepMind, and the founders of OpenAI, including Ilya Sutskever, along with many other leading researchers in machine learning. Graphcore worked with these researchers to design the IPU architecture based on the kinds of problems that they want to solve.

“All the innovators we spoke to said [using GPUs] is holding them back from new innovations,” he said. “If you look at the types of models that people are working on, they are primarily working on forms of convolutional neural networks because recurrent neural networks and other kinds of structures, [such as] reinforcement learning, don't map well to GPUs. Areas of research are being held back because there isn't a good enough hardware platform, and that's why we're trying to bring [IPUs] to market.”

Processor Development
Toon points to the development of ASIC-type accelerators that are built to accelerate specific neural networks, as well as increased interest in FPGA solutions for AI, as proof that GPUs can’t do the job well enough. There is a need, he says, for an easy-to-use processor that is designed from the ground up, specifically for machine intelligence.

“What you need to do is to extract parallelism in many different dimensions. So, rather than having an SIMD processor, what we need is a multiple-instruction, multiple-data machine,” he said. “We need to solve the problems of: ‘How can we access the memory in real time, during the compute?’ ‘How can I take pieces of data from here and there, gather that together, do the compute, and then scatter the answer back somewhere else?’ These are all the things that we have been solving with the IPU processor....MORE
If you follow the link there are three references to the entire interview. If you don't follow the link here's "Graphcore CEO Touts 'Most Complex Processor' Ever".

If interested see also:
Top 10 British Artificial Intelligence Startups

British AI Startup Graphcore Raises $200 Million From BMW, Microsoft

And related (and the reason the two types of chips are bolded above):
Chips: The Accelerator Wall—A New Problem for a Post-Moore’s Law World (GPU; ASIC; FPGA)
Watch Out Nvidia, Xilinx Is Performing (reports, beats, pops) XLNX; NVDA
Xilinx with their field-programmable gate array approach versus Nvidia's more generalist chips is an example of the type of competition experts were predicting NVDA would be facing, some over two years ago, links after the jump...
A Dip Into Chips: "AI Chip Architectures Race To The Edge"
"Why Alibaba is betting big on AI chips and quantum computing"
Hot Chips 2018 Symposium on High Performance Chips
Chips: "A Rogues Gallery of Post-Moore’s Law Options"
"Top-Rated Chipmaker Xilinx Gets Big Price-Target Hike On 5G Prospects" (XLNX; NVDA)

Wednesday, June 5, 2019

Chips: NVIDIA Begins To Embrace the Move Toward More Specialized Chips (NVDA; INTC; AMD; GOOG; XLNX)

See I told you I wasn't crazy. Something we've been babbling about for a couple years.
Some links below.
From MIT's Technology Review:
Hardware design, rather than algorithms, will help us achieve the next big breakthrough in AI. That’s according to Bill Dally, Nvidia’s chief scientist, who took the stage Tuesday at EmTech Digital, MIT Technology Review’s AI conference. “Our current revolution in deep learning has been enabled by hardware,” he said.

As evidence, he pointed to the history of the field: many of the algorithms we use today have been around since the 1980s, and the breakthrough of using large quantities of labeled data to train neural networks came during the early 2000s. But it wasn’t until the early 2010s—when graphics processing units, or GPUs, entered the picture—that the deep-learning revolution truly took off.
“We have to continue to provide more capable hardware, or progress in AI will really slow down,” Dally said.

Nvidia is now exploring three main paths forward: developing more specialized chips; reducing the computation required during deep learning; and experimenting with analog rather than digital chip architectures.

Nvidia has found that highly specialized chips designed for a specific computational task can outperform GPU chips that are good at handling many different kinds of computation. The difference, Dally said, could be as much as a 20% increase in efficiency for the same level of performance.
Dally also referenced a study that Nvidia did to test the potential of “pruning”—the idea that you can reduce the number of calculations that must be performed during training, without sacrificing a deep-learning model’s accuracy. Researchers at the company found they were able to skip around 90% of those calculations while retaining the same learning accuracy. This means the same learning tasks can take place using much smaller chip architectures.

Finally, Dally mentioned that Nvidia is now experimenting with analog computation. Computers store almost all information, including numbers, as a series of 0s or 1s. But analog computation would allow all sorts of values—such as 0.3 or 0.7—to be encoded directly. That should unlock much more efficient computation, because numbers can be represented more succinctly, though Dally said his team currently isn’t sure how analog will fit into the future of chip design.

Naveen Rao, the corporate vice president and general manager of the AI Products Group at Intel, also took the stage and likened the importance of the AI hardware evolution to the role that evolution played in biology. Rats and humans, he said, are divergent in evolution by a time scale of a few hundred million years. Despite vastly improved capabilities, however, humans have the same fundamental computing units as their rodent counterparts....
....MORE

Previously on specialized chips:
March 2019
Chips: The Accelerator Wall—A New Problem for a Post-Moore’s Law World (GPU; ASIC; FPGA)
Feb. 2019 
Watch Out NVIDIA: "Amazon, Huawei efforts show move to AI-centric chips continues"
Jan. 24
Watch Out Nvidia, Xilinx Is Performing (reports, beats, pops) XLNX; NVDA

Xilinx with their field-programmable gate array approach versus Nvidia's more generalist chips is an example of the type of competition experts were predicting NVDA would be facing, some over two years ago, links after the jump...
Dec 2018
A Dip Into Chips: "AI Chip Architectures Race To The Edge"
Sept. 2018
"Why Alibaba is betting big on AI chips and quantum computing"
Sept 2018
Hot Chips 2018 Symposium on High Performance Chips
Sept 2018
Chips: "A Rogues Gallery of Post-Moore’s Law Options"
Aug 18
Ahead of NVIDIA Earnings: The Last of the "Easy" Comparisons (NVDA)
Beating that Q3 2017 EPS, 90 cents, by double i.e. $1.80 or more, is doable but AI and data centers will have to pick up the slack from the Q1 and Q2 cryptocurrency bump that started declining with Bitmain and other miners use of ASICs rather than GPU's.

Going forward the trend toward specialist proprietary chips, see Tesla's development of their own chips etc, etc will leave NVIDIA with a couple holes in the potential addressable markets they will want to fill.

Additionally, the smaller pups, some still in stealth, are nipping at the big dog's heels, making it more expensive for NVIDIA to maintain their edge in architecture....
Aug 2018
Artificial Intelligence Chips: Past, Present and Future
May 2018
Chipmakers Battle To Power Artificial Intelligence In Cloud Data Centers" (AMD; NVDA; XLNX; INTC)
May 2018
"Xilinx Analyst Day Plays Heavy on AI" (XLNX)
May 2018
"Intel vs. Nvidia: Future of AI Chips Still Evolving" (INTC; NVDA)
Dec. 2017
“'The Ultimate Trading Machine' from Penguin Computing sets Record for Low Latency"
Oct. 2017
"The Natural Evolution of Artificial Intelligence" 

"Nvidia CEO is 'more than happy to help' if Tesla's A.I. chip doesn't pan out" (NVDA; TSLA)

And April 2017
We've said NVIDIA probably has a couple year head start but this bears watching, so to speak....
***
The only reason for Tesla to do this is that NVIDIA's chips are general purpose whereas specialized chips are making inroads in stuff like crypto mining (ASICs), Google's Tensor Processing Units (TPUs) for machine learning and Facebook's hardware efforts.

In May, 2018 we met a person who knows about this stuff:

AI VC: "We Are Here To Create"
Sometimes the competition is just plain intimidating/scary/resistance-is-futile, smart.

June 2016
Machine Learning: JP Morgan Compares Google's New Chip With NVIDIA's (GOOG; NVDA)

And many more, use the "search blog" box, upper left if interested.