Adaptation, learning and storage in analog VLSI

Adaptation and learning are key elements in biological and artificial neural systems for computational tasks of perception, classification, association, and control. They also provide an effective means to compensate for imprecisions in highly efficient analog VLSI implementations of parallel application-specific processors, which offer real-time operation and low power dissipation. The effectiveness of embedded learning and adaptive functions in analog VLSI relies on careful design of the implemented adaptive algorithms, and on adequate means for local and long-term analog memory storage of the adapted parameter coefficients. We address issues of technology, algorithms, and architecture in analog VLSI adaptation and learning, and illustrate those with examples of prototyped ASIC processors.