Recently, both industry and academia have proposed several different neuromorphic
systems to execute machine learning applications that are designed using Spiking Neural …

In resource-constrained environments, such as low-power edge devices and smart sensors,
deploying a fast, compact, and accurate intelligent system with minimum energy is …

A key challenge for utilizing spiking neural networks or spiking neuromorphic systems for
most applications is translating numerical data into spikes that are appropriate to apply as …

Spiking, neuromorphic computing systems are in a period of active exploration by the
computing community. While they feature computational expressiveness beyond both von …

Following from the original Dynamic Adaptive Neural Network Array (DANNA) model, we
propose a new digital neuromorphic architecture named DANNA 2. Through this paper, we …

Novel uses of graphical processing units for accelerated computation revolutionized the field
of high-performance scientific computing by providing specialized workflows tailored to …

We have previously proposed a novel hardware platform (SOEN) for neuromorphic
computing based on superconducting optoelectronics that presents many of the features …

A variety of neural network models and machine learning techniques have arisen over the
past decade, and their successes with image classification have been stunning. With other …

In neuromorphic computing, different learning mechanisms are being widely adopted to
improve the performance of a specific application. Among these techniques, spike-timing …

In this work we describe the design, implementation, and testing of the first neuromorphic
robot capable of obstacle avoidance, grid coverage, and targeting controlled by the second …