From Nanalyze, June 10:
What a difference a year makes. Few companies had enjoyed the sort of bull run AI chipmaker Nvidia (NVDA ) had been on, returning more than +1200% between June 2015 and June 2018, eventually hitting a market cap of about $175 billion by September 2018. Then everything went south – literally – as the market took a historic plunge in the fourth quarter, taking Nvidia with it. However, while many companies have bounced back, Nvidia has continued to languish, sitting at a valuation of about $88 billion, pretty much where it was circa May 2017 when we compared its AI chip technology against AMD (AMD). Now, over the last five years, the two chip manufacturers have returned almost identical value to investors, while a number of upstart startups have risen to also challenge Nvidia’s supremacy with new artificial intelligence chips.
In fact, it was exactly three years ago that we first introduced you to five startups building artificial intelligence chips, and then followed that up with 12 new AI chip makers in 2017. Last year, we noted that the Chinese are also gunning for Nvidia with their own homegrown artificial intelligence chips, as China seeks to dominate everything to do with AI and other emerging technologies. Now there are at least 40 different startups that have raised capital to develop AI hardware, according to research firm CB Insights, within an ecosystem of some 2,000 companies that have raised $19 billion in equity for AI-related software or services. Here’s a look at just some of the startups working on new AI chips (data taken from Crunchbase).
| Company | Location | Total Funding | Last Funding Date | Last Funding Amount |
| Bitmain | Beijing, China | $764.7M | Aug-18 | $422M |
| Horizon Robotics | Beijing, China | $700M | Feb-19 | $600M |
| Graphcore | Bristol, U.K. | $310M | Dec-18 | $200M |
| SambaNova Systems | Silicon Valley | $206M | Apr-19 | $150M |
| Wave Computing | Campbell, California | $203.3M | Nov-18 | $86M |
| Cambricon Technologies | Beijing, China | $200M | Jun-18 | $100M |
| Habana Labs | San Jose, California | $120M | Nov-18 | $75M |
| Cerebras | Silicon Valley | $112M | Jan-17 | $60M |
| ThinkForce Electronic Technology | Shanghai, China | $68 M | Dec-17 | $68M |
| ThinCI | El Dorado Hills, California | $65 M | Sep-18 | $65M |
| Groq | Silicon Valley | $62.3M | Sep-18 | $52M |
| Esperanto Technologies | Silicon Valley | $58M | Nov-18 | $58M |
| Mythic | Austin, Texas | $55.2M | Mar-18 | $40M |
| DeePhi Tech | Beijing, China | $40M | Oct-17 | $40M |
| Hailo | Tel Aviv, Israel | $24.5M | Jan-19 | $8.5M |
| Nervana Systems | San Diego, California | $24.4M | Jun-15 | $20.5M |
| Tachyum | San Jose, California | $17M | Mar-19 | $17M |
| NovuMind | Santa Clara, California | $15.2M | Dec-16 | $15.2M |
| Flex Logix Technologies | Silicon Valley | $12.4M | May-17 | $5M |
| Untether AI | Toronto, Ontario | $10M | Apr-19 | $10M |
| Cornami | Santa Clara, California | $6.5M | Nov-17 | $1.7M |
| Adapteva | Lexington, Massachusetts | $6M | Jan-14 | $3.6M |
| Koniku | Newark, California | $1.7M | Mar-18 | Undisclosed |
| Tenstorrent | Toronto, Canada | $637,500 | Dec-16 | $637,500 |
| krtkl | San Francisco, California | $160,000 | Oct-15 | $160,000 |
While few if any of these AI chip startups may be a direct threat to Nvidia at this point – its current struggles are related to the inevitable cryptocurrency crash and supply chain problems – it can’t be too far in the future where some of these new companies will start carving out market share with potentially better and cheaper hardware.....MUCH MORE
Why Does AI Need Special Chip Hardware?
The computing needs for AI applications like facial recognition are different from the hardware used in your basic laptop or smartphone, let alone the huge servers that power cloud services or supercomputers. Nvidia’s graphics processing units (GPUs) were originally designed for intense computing needs required for games and other complex software. It was a stroke of luck that they also worked well for neural networks, or AI systems that mimic the human brain in order to do things like detect patterns in data that might help stop cyberattacks or even discover new drugs....
If interested see also June 5ths:
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:....
