Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware
for information processing, capable of highly sophisticated tasks. Systems built with standard …

Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of
attention lately due to its promise of reducing the computational energy, latency, as well as …

Abstract Spiking Neural Networks (SNNs) have recently attracted significant research
interest as the third generation of artificial neural networks that can enable low-power event …

Abstract Spiking Neural Networks (SNNs) are promising for enabling low-power event-
driven data analytics. The best performing SNNs for image recognition tasks are obtained by …

The increasing demand for on-chip edge intelligence has motivated the exploration of
algorithmic techniques and specialized hardware to reduce the computing energy of current …

Historically, memory technologies have been evaluated based on their storage density, cost,
and latencies. Beyond these metrics, the need to enable smarter and intelligent computing …

Toward the long-standing dream of artificial intelligence, two successful solution paths have
been paved: 1) neuromorphic computing and 2) deep learning. Recently, they tend to …

On-chip edge intelligence has necessitated the exploration of algorithmic techniques to
reduce the compute requirements of current machine learning frameworks. This work aims …

Spiking Neural Networks (SNNs) may offer an energy-efficient alternative for implementing
deep learning applications. In recent years, there have been several proposals focused on …

The fields of brain‐inspired computing, robotics, and, more broadly, artificial intelligence (AI)
seek to implement knowledge gleaned from the natural world into human‐designed …