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 …

Biometrics such as facial features, fingerprint, and iris are being used increasingly in modern
authentication systems. These methods are now popular and have found their way into …

Spiking Neural Networks (SNNs) represent a biologically inspired computation model
capable of emulating neural computation in human brain and brain-like structures. The main …

The usage of Deep Neural Networks (DNN) on resource-constrained edge devices has
been limited due to their high computation and large memory requirement. In this work, we …

Deep learning algorithms have shown tremendous success in many recognition tasks;
however, these algorithms typically include a deep neural network (DNN) structure and a …

Binary deep neural networks, that have been implemented in resistive random access
memory (ReRAM) for storage efficiency, suffer from poor recognition performance in the …

Deep learning and neural networks have great potential while still at risk. The so-called
adversarial attacks, which apply small perturbations on input samples to fool models …

Edge computing applications have recently gained prominence as the world of internet-of-
things becomes increasingly embedded into people's lives. Performing computations at the …

Emerging nonvolatile memory based hardware neuromorphic computing systems have
enabled the implementation of general vector-matrix multiplication in a manner to fuse …