J. Xin’s research while affiliated with University of Pennsylvania and other places

A programmable analog neural computer and selected applications are described. The machine was assembled from over 100 custom VLSI modules containing neurons, synapses, routing switches, and programmable synaptic time constants. Connection symmetry and modular construction allow expansion to arbitrary size. The network runs in real-time analog mode. Connection architecture as well as neuron and synapse parameters are controlled by a digital host that monitors the network performance through a digital/analog interface. Programming and monitoring software has been developed. Several application examples, including the dynamic decomposition of acoustical patterns, are described. The machine is intended to real-time, real-world computations. In current configuration its maximal speed is equivalent to that of a digital machine capable of more than 10¹² FLOPS

A multichip, programmable analog neural network for real-time dynamic computations is described. The network's interconnection structure, the neuron characteristics, synaptic connections, and synaptic time constant are modifiable. The chips are designed to allow a modular and expandable gross architecture that can be adjusted to the complexity of the task. The network operates fully analog in real time. However, a digital host is used to set the network parameters and monitor the neuron outputs. A prototype neural computer consisting of 72 neurons has been assembled and tested. The network has been successfully configured for several applications and found to have a performance that is equivalent to a digital machine of 10¹¹ FLOPS

This paper gives an overview of the principles and hardware realizations of artificial neural networks. The first section describes the operation of neural networks, using simple examples to illustrate some of its key properties. Next the different architectures are described, including single and multiple perceptron networks, Hopfield and Kohonen nets. A brief discussion of the learning rules employed in feedforward and feedback networks follows. The final section discusses hardware implementations of neural systems with emphasis on analog VLSI. Different approaches for the realizations of neurons and synapses are described. A brief comparison between analog and digital techniques is given.