The demand for intensive computational power for real time information processing applications is rapidly increasing. These information systems are anticipated to support tera operations-per-second in 1996 and beyond. Artificial neural networks represent one of the approaches to enhance computational capabilities in real-time information processing. Analog or mixed digital/analog VLSI neural network processors have been widely developed and are preferred over digital solutions particularly for dedicated applications. We, therefore, see a strong connection between advances in analog VLSI design and the rate of progress of application driven neural network hardware. This paper is devoted primarily to advances and trends in analog VLSI, and discusses the challenges as well as the tremendous potential of analog VLSI neural nets to address real world problems. We also present an interdisciplinary view on VLSI in general and on analog VLSI in particular.
"Analog VLSI neural systems: trends and challenges", Proc. SPIE 2492, Applications and Science of Artificial Neural Networks, (6 April 1995); doi: 10.1117/12.205168; https://doi.org/10.1117/12.205168