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 …

Deep learning has become ubiquitous, touching daily lives across the globe. Today,
traditional computer architectures are stressed to their limits in efficiently executing the …

Deep neural networks are biologically inspired class of algorithms that have recently
demonstrated the state-of-the-art accuracy in large-scale classification and recognition …

The use of deep learning (DL) to real-world applications, such as computer vision, speech
recognition, and robotics, has become ubiquitous. This can be largely attributed to a virtuous …

Although spiking neural networks (SNNs) take benefits from the bioplausible neural
modeling, the low accuracy under the common local synaptic plasticity learning rules limits …

Conventional von Neumann architecture suffers from bottlenecks in computing performance
and power consumption due to frequent data exchange between memory and processing …

The research work presented in this paper deals with the fault analysis in hardware-
implemented Spiking Neural Networks with special emphasis on circuits designed to …

With high efficiency and low energy consumption, bio-inspired artificial neuromorphic
systems are regarded as the next generation of computing methods and have attracted …

Advancements in artificial intelligence (AI) and big data have highlighted the limitations of
traditional von Neumann architectures, such as excessive power consumption and limited …

Binary neural networks (BNNs) can transform multiply-and-accumulate (MAC) operations
into XNOR and accumulation (XAC), which has been proven to greatly reduce the hardware …