Spiking Neural Networks Hardware Implementations and Challenges: a Survey

Abstract

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 bringing closer the memory and the computational elements to efficiently evaluate machine-learning algorithms. Recently, Spiking Neural Networks, a generation of cognitive algorithms employing computational primitives mimicking neuron and synapse operational principles, have become an important part of deep learning. They are expected to improve the computational performance and efficiency of neural networks, but are best suited for hardware able to support their temporal dynamics. In this survey, we present the state of the art of hardware implementations of spiking neural networks and the current trends in algorithm elaboration from model selection to training mechanisms. The scope of existing solutions is extensive; we thus present the general framework and study on a case-by-case basis the relevant particularities. We describe the strategies employed to leverage the characteristics of these event-driven algorithms at the hardware level and discuss their related advantages and challenges.

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References in corpus (13)

• Deep Learning in Neural Networks: An Overview
• Training and Operation of an Integrated Neuromorphic Network Based on Metal-Oxide Memristors
• Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks
• Memory and information processing in neuromorphic systems
• STDP-based spiking deep convolutional neural networks for object recognition
• Optoelectronic Reservoir Computing
• The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception
• SuperSpike: Supervised learning in multi-layer spiking neural networks
• First-spike based visual categorization using reward-modulated STDP
• Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines
• Bio-inspired Unsupervised Learning of Visual Features Leads to Robust Invariant Object Recognition
• An Accelerated LIF Neuronal Network Array for a Large Scale Mixed-Signal Neuromorphic Architecture
• Tactics to Directly Map CNN graphs on Embedded FPGAs

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• Xpikeformer: Hybrid Analog-Digital Hardware Acceleration for Spiking Transformers
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• Energy efficiency analysis of Spiking Neural Networks for space applications
• SpikeExplorer: hardware-oriented Design Space Exploration for Spiking Neural Networks on FPGA
• Brain-inspired Computing Based on Deep Learning for Human-computer Interaction: A Review
• Implementation of binary stochastic STDP learning using chalcogenide-based memristive devices
• Towards Crossing the Reality Gap with Evolved Plastic Neurocontrollers