A mixed mode self-programming neural system-on-chip for real-time applications

The paper provides an overview of the development of a self-learning computing chip in the new 0.18 micron copper technology. The chip realizes an architecture that achieves the task of self-learning execution times in microseconds to milliseconds. The core consists of basic building blocks of 4-quadrant multipliers, transconductance amplifiers, and active load resistances, for analog (forward-) network processing and learning modules. Superimposed on the processing network are digital memory and control modules composed of D-flip-flops, ADC, multiplying D/A converter, and comparators for parameter (weight) storage, logical control and analog/digital conversions. The single system-on-chip design impacts several domains of critical applications that include nano-scale bio-technology, automotive sensing, central and actuation, wireless communications, image feature extraction and pattern matching, etc.