Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices,
and models that contrast the pervasive von Neumann computer architecture. This …

Deep neural networks such as Convolutional Networks (ConvNets) and Deep Belief
Networks (DBNs) represent the state-of-the-art for many machine learning and computer …

A mixed-signal very large scale integration (VLSI) chip for large scale emulation of spiking
neural networks is presented. The chip contains 2400 silicon neurons with fully …

The ongoing revolution in Deep Learning is redefining the nature of computing that is driven
by the increasing amount of pattern classification and cognitive tasks. Specialized digital …

We present a design framework for neuromorphic architectures in the nano-CMOS era. Our
approach to the design of spiking neurons and STDP learning circuits relies on parallel …

Biological systems process visual input using a distributed representation, with different
areas encoding different aspects of the visual interpretation. While current engineering …

We present a multichip, mixed-signal VLSI system for spike-based vision processing. The
system consists of an 80× 60 pixel neuromorphic retina and a 4800 neuron silicon cortex …

In this paper, we propose an analog CMOS circuit which achieves spiking neural networks
with spike-timing dependent synaptic plasticity (STDP). In particular, we propose a STDP …

Rapid design time, low cost, flexibility, digital precision, and stability are characteristics that
favor FPGAs as a promising alternative to analog VLSI based approaches for designing …

Providing highly flexible connectivity is a major architectural challenge for hardware
implementation of reconfigurable neural networks. We perform an analytical evaluation and …