Semiconductor Engineering .:. What's Next In Neural Networking?

Faster chips, more affordable storage, and open libraries are giving neural network new momentum, and companies are now in the process of figuring out how to optimize it across a variety of markets. The roots of neural networking stretch back to the late 1940s with Claude Shannon's Information Theory, but until several years ago this technology made relatively slow progress. The rush toward autonomous vehicles -- which relies on neural networking to collect data from many sensors -- changed all of that. Work is underway by established companies, startups, and universities around the globe, and funding is pouring into neural networking, as well as related markets such as embedded vision, machine learning, and artificial intelligence. "Mass market economics, increased processing power and improving computational vision techniques equals opportunities for new mass markets to be created," said Tim Ramsdale, general manager of the Imaging and Vision Group at ARM. "But all of this has to be done in real time. Having lights turn on as soon as you appear at the door is critical. That means a minimum of 30 frames per second, and preferably 60 frames per second. To do that you have to have processing at the edge, and processing at the edge means low power."