Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

Abstract

The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge. This can facilitate the advancement of medical Internet of Things (IoT) systems and Point of Care (PoC) devices. In this paper, we provide a tutorial describing how various technologies including emerging memristive devices, Field Programmable Gate Arrays (FPGAs), and Complementary Metal Oxide Semiconductor (CMOS) can be used to develop efficient DL accelerators to solve a wide variety of diagnostic, pattern recognition, and signal processing problems in healthcare. Furthermore, we explore how spiking neuromorphic processors can complement their DL counterparts for processing biomedical signals. The tutorial is augmented with case studies of the vast literature on neural network and neuromorphic hardware as applied to the healthcare domain. We benchmark various hardware platforms by performing a sensor fusion signal processing task combining electromyography (EMG) signals with computer vision. Comparisons are made between dedicated neuromorphic processors and embedded AI accelerators in terms of inference latency and energy. Finally, we provide our analysis of the field and share a perspective on the advantages, disadvantages, challenges, and opportunities that various accelerators and neuromorphic processors introduce to healthcare and biomedical domains.

arXiv commentAccepted by IEEE Transactions on Biomedical Circuits and Systems (21 pages, 10 figures, 5 tables)

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• Organic electrochemical neurons and synapses with ion mediated spiking
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• An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG
• Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligence
• MemTorch: An Open-source Simulation Framework for Memristive Deep Learning Systems
• Seizure Detection and Prediction by Parallel Memristive Convolutional Neural Networks
• Neuromorphic weighted sums with magnetic skyrmions
• Towards Memristive Deep Learning Systems for Real-time Mobile Epileptic Seizure Prediction
• Improving the Accuracy of Analog-Based In-Memory Computing Accelerators Post-Training
• MC-QDSNN: Quantized Deep evolutionary SNN with Multi-Dendritic Compartment Neurons for Stress Detection using Physiological Signals
• Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Survey
• 2022 Roadmap on Neuromorphic Computing and Engineering