Abstract-We propose and analyze analog VLSI implementations of neural networks in which both the neural cells and the synapses are realized using Operational Transconductance Amplifiers (OTAs). These circuits have inherent advantages of immunity to noise, very high input/output impedances, differential architecture with automatic inversion, and density. An efficient on-chip technique for weight adaptation and for adjusting the gain of OTA-based neurons is proposed. Power and area requirements are obtained. These building blocks can be used to efficiently construct several types of networks including Hopfield networks, Boltzmann machines and cellular networks. Circuit simulations using MTIME show that small Hopfield memories converge in about a psec.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.