A synthetic neural integrated circuit
Lex A. Akers, Mark R. Walker, R. Grondin, David K. Ferry · 1989
Integrated circuits are approaching biological complexity in device count. Biological systems are fault tolerant, adaptive, and trainable, and the possibility exists for similar characteristics in ICs. The authors report a limited-interconnect, highly layered synthetic neural network that implements these ideals. These networks are specifically designed to scale to tens of thousands of processing elements on current production size dies. A compact analog cell, a training algorithm, and a limited-interconnect architecture which has demonstrated neuromorphic behavior are described