Neuromorphic computing covers a diverse range of approaches to information processing
all of which demonstrate some degree of neurobiological inspiration that differentiates them …

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 …

Spiking neural networks (SNNs) can potentially offer an efficient way of doing inference
because the neurons in the networks are sparsely activated and computations are event …

Neuromorphic networks of artificial neurons and synapses can solve computationally hard
problems with energy efficiencies unattainable for von Neumann architectures. For image …

We present the first gesture recognition system implemented end-to-end on event-based
hardware, using a TrueNorth neurosynaptic processor to recognize hand gestures in real …

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 …

Solving real world problems with embedded neural networks requires both training
algorithms that achieve high performance and compatible hardware that runs in real time …

Deep-learning neural networks such as convolutional neural network (CNN) have shown
great potential as a solution for difficult vision problems, such as object recognition. Spiking …

Neuromorphic computing is henceforth a major research field for both academic and
industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at …

Inspired by the brain's structure, we have developed an efficient, scalable, and flexible non–
von Neumann architecture that leverages contemporary silicon technology. To demonstrate …