Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

• Published in: IEEE transactions on biomedical circuits and systems Vol. 14; no. 6; pp. 1138 - 1159
• Main Authors: Azghadi, Mostafa Rahimi, Lammie, Corey, Eshraghian, Jason K., Payvand, Melika, Donati, Elisa, Linares-Barranco, Bernabe, Indiveri, Giacomo
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accelerators; Algorithms; Biomedical materials; CMOS; CMOS technology; Computer vision; Deep learning; deep neural networks; Diagnostic systems; Domains; Electromyography; Field programmable gate arrays; Firing pattern; FPGA; Hardware; Health care; healthcare; Internet of medical things; Internet of Things; Latency; Machine learning; Medical electronics; Medical Internet of Things; medical IoT; Medical services; Memory devices; memristor; Memristors; Network latency; Neural networks; Neuromorphic engineering; neuromorphic hardware; Pattern recognition; point-of-care; Processors; RRAM; Signal processing; Spiking; spiking neural networks
• ISSN: 1932-4545; 1940-9990; 1940-9990
• DOI: 10.1109/TBCAS.2020.3036081

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 Th...