Exploring Neuromorphic Computing Based on Spiking Neural Networks: Algorithms to Hardware

Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving lot of attention lately due to its promise reducing computational energy, latency, as well learning complexity artificial neural networks. Taking inspiration from neuroscience, this interdisciplinary field performs multi-stack optimization across devices, circuits, and algorithms by providing an end-to-end approach achieving brain-like efficiency machine intelligence. On one side, neuromorphic computing introduces new algorithmic paradigm, known Spiking Neural Networks (SNNs), which significant shift standard deep transmits information spikes (“1” or “0”) rather than analog values. This has opened up novel research directions formulate methods represent data spike-trains, develop neuron models that can process over time, design for event-driven dynamical systems, engineer network architectures amenable sparse, asynchronous, achieve lower power consumption. other parallel thrust focuses on development efficient platforms algorithms. Standard accelerators are workloads not particularly suitable handle processing multiple timesteps efficiently. To effect, researchers have designed hardware rely sparse computations matrix operations. While most large-scale systems been explored based CMOS technology, recently, Non-Volatile Memory (NVM) technologies show toward implementing bio-mimetic functionalities single devices. In article, we outline several strides spiking networks (SNNs) taken recent past, present our outlook challenges needs overcome make bio-plausibility route successful one.