A Survey of Neuromorphic Computing and Neural Networks in Hardware

Abstract

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture. This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems. The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities. In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history. We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications. We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled. The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed.

References in corpus (8)

• Training and Operation of an Integrated Neuromorphic Network Based on Metal-Oxide Memristors
• Filamentary Switching: Synaptic Plasticity through Device Volatility
• Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition
• Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms
• A Neuromorphic VLSI Design for Spike Timing and Rate Based Synaptic Plasticity
• Event management for large scale event-driven digital hardware spiking neural networks
• Gibbs Sampling with Low-Power Spiking Digital Neurons
• A Biological-Realtime Neuromorphic System in 28 nm CMOS using Low-Leakage Switched Capacitor Circuits

Cited by in corpus (17)

• Neuromorphic Hardware learns to learn
• Design Space Exploration of Hardware Spiking Neurons for Embedded Artificial Intelligence
• Constant-Depth and Subcubic-Size Threshold Circuits for Matrix Multiplication
• Brain-Inspired Hardware for Artificial Intelligence: Accelerated Learning in a Physical-Model Spiking Neural Network
• Spike-based primitives for graph algorithms
• Quantum Technologies: A Review of the Patent Landscape
• Neuromorphic Processing and Sensing: Evolutionary Progression of AI to Spiking
• Combining Spiking Neural Network and Artificial Neural Network for Enhanced Image Classification
• Benchmarking Physical Performance of Neural Inference Circuits
• Spintronics for neuromorphic computing
• Rebooting Neuromorphic Hardware Design -- A Complexity Engineering Approach
• A Non-equilibrium Thermodynamic Framework of Consciousness
• A Memristor-Based Optimization Framework for AI Applications
• A Neural Architecture Search based Framework for Liquid State Machine Design
• MERIT: Tensor Transform for Memory-Efficient Vision Processing on Parallel Architectures
• Cerebral cortical communication overshadows computational energy-use, but these combine to predict synapse number
• Building Reservoir Computing Hardware Using Low Energy-Barrier Magnetics