CAREER: Analog VLSI Integrated Circuits for Real-Time Neural Control

Georgia Tech Research Corporation, Atlanta GA

Investigators

Abstract

0093915 Hasler This project will develop a new class of chips capable of high-thruput learning on the chip itself, implementing new concepts of neural network learning which should allow the chips to learn how to control manufacturing plants or vehicles in a very generalized way, applicable to a wide class of tasks. As a testbed, the project will develop neural network learning algorithms and chips to improve the performance of advanced semiconductor fabrication designs being developed by the PI's collaborators at Georgia Tech. If successful, this work could significantly accelerate the use of learning systems for engineering applications in general. It could eventually allow a substantial improvement in effective computational throughput per chip, above and beyond the improvements possible through Moore's Law (the increase in the physical feature density and speed of chips), for a very broad range of information processing tasks. This project builds on the PI's prior work developing analog computable memories, or analog computing arrays, where instead of storing the analog values to be used by external processors, he uses the memory element itself to perform the computation. These systems are based on arrays of dense floating-gate transistors that provide nonvolatile storage, compute a product between stored weights and inputs, allow for programming that does not affect the computation, and adapt over time based on the information flowing through the chip. In principle, this technology permits the development of general purpose chips with throughput comparable to that of dedicated analog ASIC chips, but with a kind of universal flexibility due to the adaptation of the weights and the universal approximation capabilities of the underlying architectures.