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Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications


Azghadi, Mostafa Rahimi; Lammie, Corey; Eshraghian, Jason K; Payvand, Melika; Donati, Elisa; Linares-Barranco, Bernabe; Indiveri, Giacomo (2020). Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications. IEEE Transactions on Biomedical Circuits and Systems, 14(6):1138-1159.

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.

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Physical Sciences > Biomedical Engineering
Physical Sciences > Electrical and Electronic Engineering
Uncontrolled Keywords:Electrical and Electronic Engineering, Biomedical Engineering
Language:English
Date:1 December 2020
Deposited On:16 Feb 2021 09:17
Last Modified:17 Feb 2021 21:02
Publisher:Institute of Electrical and Electronics Engineers
ISSN:1932-4545
OA Status:Hybrid
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1109/tbcas.2020.3036081
Project Information:
  • : FunderFP7
  • : Grant ID201900
  • : Project TitleMIREG - Identifying novel regulatory mechanisms of miRNA functions
  • : FunderH2020
  • : Grant ID724295
  • : Project TitleNeuroAgents - Neuromorphic Electronic Agents: from sensory processing to autonomous cognitive behavior
  • : FunderH2020
  • : Grant ID824164
  • : Project TitleHERMES - Hybrid Enhanced Regenerative Medicine Systems
  • : FunderH2020
  • : Grant ID871371
  • : Project TitleMeM-Scales - Memory technologies with multi-scale time constants for neuromorphic architectures
  • : FunderFP7
  • : Grant ID201804
  • : Project TitleEUCILIA - Pathophysiology of rare diseases due to ciliary dysfunction: nephronophthisis, Oral-facial-digital type 1 and Bardet-Biedl syndromes

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