Analog VLSI on-chip learning Neural Networks represent a mature technology for a large number of applications involving industrial as well as consumer appliances. This is particularly the case when low power consumption, small size and/or very high speed are required. This approach exploits the computational features of Neural Networks, the implementation efficiency of analog VLSI circuits and the adaptation capabilities of the on-chip learning feedback schema.
Many experimental chips and microelectronic implementations have been reported in the literature based on the research carried out over the last few years by several research groups. The author presents and discusses the motivations, the system and circuit issues, the design methodology as well as the limitations of this kind of approach. Attention is focused on supervised learning algorithms because of their reliability and popularity within the neural network research community. In particular, the Back Propagation and Weight Perturbation learning algorithms are introduced and reviewed with respect to their analog VLSI implementation.
Finally, the author also reviews and compares the main results reported in the literature, highlighting the efficiency and the reliability of the on-chip implementation of these algorithms.