5 Startups Building Artificial Intelligence Chips
The first thing we asked when we were turned on to this niche was, what’s an artificial intelligence chip? It’s best to first think about what artificial intelligence software requires which is a great deal of processing speed, then a great deal of power in order to feed that processing speed. However, it’s not just speed and low power that matter, it’s also the way the processor functions. This excerpt from MIT Technology review explains why we can’t just use a high-end Intel processor chip for artificial intelligence:
While a top-of-the-line Intel processor packs more than enough punch to run sprawling financial spreadsheets or corporate operations software, chips optimized for deep learning break particular types of problems—such as understanding voice commands or recognizing images—into millions of bite-size chunks. Because GPUs like Nvidia’s consist of thousands of tiny processor cores crammed together on one slice of silicon, they can handle thousands of these chunks simultaneously. Assigning an Intel processor to such work would be a huge waste of resources, since each of these processors contains a few dozen cores that are designed to run complex algorithms. Deep-learning chips don’t need to do that much thinking to handle all those micro-tasks.
The term used above called “GPU” is an acronym that stands for Graphical Processing Units which are chips that are used in artificial intelligence applications like image recognition and computer vision. The actual way you architect a chip can be optimized for specific artificial intelligence tasks like image recognition, voice recognition, or big data analysis of any kind. In the case of deep learning, you use artificial neural networks which simulate the behavior of the brain by creating simulated neurons. Here are 5 companies that are building chips and hardware solutions that promise to optimize artificial intelligence tasks.
KnuEdge really isn’t a startup since they’ve been operating in stealth mode for 10 years now. Founded by a former Head of NASA, KnuEdge recently came out of stealth to let the world know that they’ve taken in $100 million from unnamed investors to develop a new “neural chip” which the company describes as follows:
KNUPATH delivers chip technology based on LambaFabric™, which will enable neural computing through an entirely different architecture than the GPUs, CPUs and FPGAs currently on the market. LambdaFabric is inherently designed to scale up to 512,000 devices in the most demanding computing environments, has rack-to-rack latency of only 400ns, and low-wattage, 256-core processors. KNUPATH technology is entirely new and built on biological principles, and will reset the standard for chip/system-level compute in data centers and consumer devices.
That chip technology is supposed to offer a 2X to 6X performance advantage over similar chips and the Company has already generated revenue from sales of their prototype systems. KnuEdge is organizing into 3 separate companies under the “KnuEdge umbrella”. KnuPath is their chip offering. KnuVerse delivers proven military-grade voice recognition and authentication technology. Knurld.io is a public cloud API service that allows developers to simply incorporate voice authentication into their proprietary products. KnuEdge claims that it is now possible to authenticate to computers, web and mobile apps and IoT devices with only a few words spoken into a microphone. How nice would it be to never have to remember a password ever again?
UPDATE: 8/24/2018 – KnuEdge may have gone bust, with speculation being raised that they couldn’t find any more money. You can read more about it in this May 2018 article by the San Diego Business Journal, and the fact that their website no longer functions leads us to believe that KnuEdge may have gone kaput.
Founded in 2014, San Diego based startup Nervana Systems has taken in $24.4 million in funding from 20 different investors, one of which was the highly respected firm of Draper Fisher Jurvetson (DFJ). The Nervana Engine (coming in 2017) is an application specific integrated circuit (ASIC) that is custom-designed and optimized for deep learning. This solution is made possible by a new memory technology called High Bandwidth Memory that is both high-capacity and high-speed, providing 32 GB of on-chip storage and a blazingly fast 8 Tera-bits per second of memory access speed. The Company also presently provides an artificial intelligence service “in the cloud” which they claim is the world’s fastest and which is presently being used by financial services institutions, healthcare providers, and government agencies. The idea here is that their new chip will assure the Nervana Cloud platform remains the fastest in the years to come.
Founded in 2015, Chinese based startup Horizon Robotics has taken in an undisclosed amount of seed funding from investors that included Sequoia and legendary venture capitalist Yuri Milner. They are setting out to build a one-stop-shop AI solution which they described as “define the “brain”of things, for convenience, safety and fun”. We’re not sure if there’s a bit of Engrish going on here but the value proposition sounds pretty compelling considering that some very big names have placed bets on it.
Founded in 2015, krtkl wants to create “a tiny wireless computer for creating something truly different“. Techies will drool over the Snickerdoodle, a dual-core ARM processor, FPGA, Wi-Fi, Bluetooth, and starting at just $65, “the smallest, baddest, most affordable platform for powering things like robots, drones, and computer vision”. The product is actually being funded by a crowdfunding campaign that raised over $160,000. We’ve warned readers about crowdfunding before. In the case of krtkl, their latest update says that they’ve received the alpha versions of Snickerdoodle and will be shipping them out shortly
Eyeriss actually isn’t a startup yet but we couldn’t exclude it from this list given that it’s being developed by MIT and receiving extensive media coverage. Eyeriss is actually a piece of hardware that is an energy-efficient deep convolutional neural network (CNN) accelerator. Here’s a diagram for all you nerds out there that actually know what that means:
The main takeaway here is the hardware depicted above is more efficient and 10X faster and consumes 10X less power than currently available graphical processing units GPUs. Partially funded by Darpa, the MIT team led by Vivienne Sze unveiled the chip at a conference this year which was the first time that a state-of-the-art neural network was demonstrated on a custom chip.
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