I recently caught up with Ian Hogarth and Nathan Benaich, who each year produce The State of AI Report, a must-read snapshot of how commercial applications of A.I. are evolving. Benaich is the founder of Air Street Capital, a solo venture capital fund that is one of the savviest early-stage investors in A.I.-based startups I know. Hogarth is the former co-founder of concert discovery app Songkick and has since go on to become a prominent angel investor as well one of the founders behind the founder-lead European venture capital platform Plural. There's always a lot to digest in their report. But one of the key takeaways from this year's State of AI is that concerns established tech giants and their affiliated A.I. research labs would monopolize the development of A.I. have been proven, if not exactly wrong, then at least premature. While it is true that Alphabet (which has both Google Brain and Deepmind in its stable), Meta, Microsoft, and OpenAI (which is closely partnered now with Microsoft) are building large "foundational models" for natural language processing and image and video generation, they are hardly the only players in the game.

"Startups" in semiconductor chip design space had been a rarity since the dot-com crash in the early 2000s. Chip design requires massive development cost as design cycles are multi-year long with dependence on (1) expensive EDA (Electronic Design Automation) tools for design and (2) foundries for manufacturing -- both of which are highly advanced technologies with very few players in the world. Long design cycles from the conception of an architecture specification to its tapeout (tapeout is when a chip design is frozen & sent to a semiconductor foundry for manufacturing) plus time it takes to develop a SW stack to program new architectures further delays the point of revenue generation for such companies. Initial high investment costs with delayed revenue and delayed improvement in gross-margin had caused major market consolidations after the 2000 dot-com crash and had made semiconductor chip startups less attractive for venture capital funding. However the advent of AI in the last 8 years with its unique computational requirement has exposed newer opportunities for domain-specific ASICs to be, once again, a high-risk-high-gain proposition for venture funding. Introduction of Tensor Processing Unit (TPU), which is a chip designed specifically for Deep Learning (DL constitutes most of AI these days), by Google in 2017 demonstrated the possibility of building a domain-specific chip solution by a new player (new in terms of building ASICs) and cross validated the presence of a lucrative market for investors.

As the industry grapples with the best way to accelerate AI performance to keep up with requirements from cutting-edge neural networks, there are many startup companies springing up around the world with new ideas about how this is best achieved. This sector is attracting a lot of venture capital funding and the result is a sector rich in not just cash, but in novel ideas for computing architectures. Here at EETimes we are currently tracking around 60 AI chip startups in the US, Europe and Asia, from companies reinventing programmable logic and multi-core designs, to those developing their own entirely new architectures, to those using futuristic technologies such as neuromorphic (brain-inspired) architectures and and optical computing. Here is a snapshot of ten we think show promise, or at the very least, have some interesting ideas. We've got them categorized by where in the network their products are targeted: data centers, endpoints, or AIoT devices.

Rumors are rife that Intel is in talks to acquire Israeli AI accelerator startup Habana Labs. Intel is reportedly considering a purchase price of ranging anywhere from $1 billion to $2 billion, according to the Israeli publication Calcalist, who broke the story earlier this week. If it's true, it would be a surprising move, given that Habana competes with Intel acquisition Nervana. Nervana, based in San Diego, was purchased by Intel back in August of 2016 for a sum believed to be around $400 million. Intel acquired another AI chip startup, Movidius, the following month (Movidius' product line is aimed at computer vision in edge devices).

Advances in computing, from speedier processors to cheaper data storage, helped ignite the new AI era. Now demand for even faster, more energy-efficient AI models is driving a wave of innovation in semiconductors.

I spent 12 years in the microchip business, specifically from 2000 to 2012. My friends and I built our own chip startup in 2004 and sold the company in 2008, before the Lehman shock. By the time of the exit of my startup, semiconductor startups were disappearing, due to the departure of VC investments. I remember the last massive wave of VC semiconductor investments, into a now largely forgotten standard called UWB (Ultra-wideband). That means all founders got burnt too.

Deep learning, an artificial intelligence-based technology that requires increased computer power and speed to support the latest algorithms, is driving a host of startups to develop AI-specific chips. According to a New York Times report, venture capitalists have now shifted focus on chip startups to help them open up a new revenue stream and become a dominant force in this emerging segment. The NYT report said that close to 45 startups were involved in developing chips that can handle a series of tasks, from speech recognition to boost the hardware requirements of self-driving cars. While the startups may not be in a position to challenge the dominance of chip majors such as Intel, Qualcomm and Nvidia, their focus will be on finding a niche to make their businesses profitable. In 2017, venture capital firms invested over $1.5 billion in such startups, with five of them raising more than $100 million, each. This was nearly two times the investment compared to 2015.

By Cade Metz SAN FRANCISCO: For years, tech industry financiers showed little interest in startup companies that made computer chips. How on earth could a startup compete with a goliath like Intel, which made the chips that ran more than 80 percent of the world's personal computers? Even in the areas where Intel didn't dominate, like smartphones and gaming devices, there were companies like Qualcomm and Nvidia that could squash an upstart. But then came the tech industry's latest big thing -- artificial intelligence. AI, it turned out, works better with new kinds of computer chips.

In July, China's government issued a sweeping new strategy with a striking aim: draw level with the US in artificial intelligence technology within three years, and become the world leader by 2030. A call for research projects from China's Ministry of Science and Technology posted online last month fills in some detail on the government's plans. And it puts Silicon Valley chipmaker Nvidia, the leading supplier of silicon for machine-learning projects, in the cross hairs. The Ministry of Science and Technology document lays out 13 "transformative" technology projects where it wants to put government money in coming months, hoping for delivery by 2021. One is to invent new chips to run artificial neural networks, the form of software propelling the AI ambitions of Google and other tech companies.

Nigel Toon, the cofounder and CEO of Graphcore, a semiconductor startup based in the U.K., recalls that only a couple of years ago many venture capitalists viewed the idea of investing in semiconductor chips as something of joke. "You'd take an idea to a meeting," he says, "and many of the partners would roll about on the floor laughing." Now some chip entrepreneurs are getting a very different reception. Instead of rolling on the floor, investors are rolling out their checkbooks. Venture capitalists have good reason to be wary of silicon, even though it gave Silicon Valley its name.