The market for specialty silicon that enables companies to run artificial intelligence models on battery-sipping and relatively constrained devices is flush with funds and ideas. Two new startups have entered the arena, each proposing a different way to break down the computing-intensive tasks of recognizing wake words, identifying people, and other jobs that are built on neural networks.
Perceive, which launched this week, and Kneron (pronounced neuron), which launched in March, are relying on neural networks at the edge to reduce bandwidth, speed up results, and protect privacy. They join a dozen or more startups all trying to bring specialty chips to the edge to make the IoT more efficient and private.
Perceive was spun out of Xperi, a semiconductor company that has built hundreds of AI models to help identify people, objects, wake words, and other popular use cases for edge AI. Two-year-old Perceive has built a 7mm x 7mm chip designed to run neural networks at the edge, but it does so by changing the way the training is done so it can build smaller models that are still accurate.
In general, when a company wants to run neural networks on an edge chip, it must make that model smaller, which can reduce accuracy. Designers also build special sections of the chip that can handle the specific type of math required to run the convolutions used in running a neural network. But Perceive threw all of that out the window, instead turning to information theory to build efficient models.
Information theory is all about finding the signal in a bunch of a noise. When applied to machine learning it is used to ascertain which features are relevant in figuring out if an image is a dog or a cat, or if an individual person is me or my husband. Traditional neural networks are trained by giving a computer tens or hundreds of thousands of images and letting them ascertain which elements are most important when it comes to determining what an object or person is.
Perceive’s methodology requires less training data, and CEO Steve Teig says that its end models are smaller, which is what allows them to run efficiently on a lower-power chip. The result of the Perceive training is expressed in PyTorch, a common machine learning framework. The company currently offers a chip as well as a service that will help generate custom models. Perceive has also developed hundreds of its own models based on the work done by Xperi.
According to Teig, Perceive has already signed two “substantial customers” — neither of which can be named — and is in talks with connected device makers ranging from video doorbells to toy companies.
The other chip startup tackling machine learning is Kneron, formed in 2015. It has built a chip that can reconfigure an element on it specifically for the type of machine learning model it needs to run. When an edge chip has to run a machine learning model it needs to do a lot of math, which has led chipmakers to put special coprocessors on the chip that can handle a type of math known as matrix multiplication. (The Perceive method of training models doesn’t require matrix multiplication.)
This flexibility, and the promise it has to enable devices to run local AI, has led Kneron to raise $73 million. Eventually, Kneron hopes to be able to tackle learning at the edge, with CEO Albert Liu promising that the company might be able to offer simplified learning later this year. (Today, all edge AI chips can only match inputs against an existing AI model, as opposed to taking input from the environment and creating a new model.)
Both Perceive and Kneron are riding high on the promise of delivering more intelligence to products that don’t need to stay connected to the internet. As privacy, power management, and local control continue to rise in importance, the two companies are joining a host of startups trying to make their hardware the next big thing in silicon.