Neuromorphic computing has become an attractive candidate for emerging computing
platforms. It requires an architectural perspective, meaning the topology or hyperparameters …

The analog circuit design of the memristive neural network (MNN), which can automatically
perform the online learning algorithm, is an open question. In this article, a memristive self …

In this paper, we introduce RISP, a reduced instruction spiking processor. While most
spiking neuroprocessors are based on the brain, or notions from the brain, we present the …

Spiking neural networks (SNN) offer a power efficient, biologically plausible learning
paradigm by encoding information into spikes. The discovery of the memristor has …

In this paper, in an effort to implement an unsupervised learning algorithm for silicon
neurons, we present a mixed-signal Leaky Integrate-And-Fire (LIF) neuron with two different …

Neuromorphic computing promises orders of magnitude improvement in energy efficiency
compared to the traditional von Neumann computing paradigm. The goal is to develop an …

Reservoir computing (RC) is a neural network paradigm that aims to significantly reduce the
training cost of recurrent neural networks. Physical reservoir computing is the hardware …

Spiking neural networks (SNN) present a biologically plausible energy efficient learning
platform by encoding data into sparse spiking events. The discovery of memristor, a …

Computing systems that are capable of performing human-like cognitive tasks have been an
area of active research in the recent past. However, due to the bottleneck faced by the …