Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-
art algorithms in Machine Learning (ML), speech recognition, computer vision, natural …

The new era of cognitive computing brings forth the grand challenge of developing systems
capable of processing massive amounts of noisy multisensory data. This type of intelligent …

Computers have undergone tremendous improvements in performance over the last 60
years, but those improvements have significantly slowed down over the last decade, owing …

Braindrop is the first neuromorphic system designed to be programmed at a high level of
abstraction. Previous neuromorphic systems were programmed at the neurosynaptic level …

The explosion of “big data” applications imposes severe challenges of speed and scalability
on traditional computer systems. As the performance of traditional Von Neumann machines …

Spiking neural networks (SNNs) incorporating biologically plausible neurons hold great
promise because of their unique temporal dynamics and energy efficiency. However, SNNs …

Neuromorphic computing was originally referred to as the hardware that mimics neuro-
biological architectures to implement models of neural systems. The concept was then …

Spiking neural networks (SNNs) can be run on neuromorphic devices with ultra-high speed
and ultra-low energy consumption because of their binary and event-driven nature …

As transistors shrink to nanoscale dimensions, trapped electrons--blocking" lanes" of
electron traffic--are making it difficult for digital computers to work. In stark contrast, the brain …

The exponential increase in data over the last decade presents a significant challenge to
analytics efforts that seek to process and interpret such data for various applications. Neural …