TY - JOUR
AU - 
AB - 1138 IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, VOL. 14, NO. 6, DECEMBER 2020 Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications Mostafa Rahimi Azghadi , Senior Member, IEEE, Corey Lammie , Student Member, IEEE, Jason K. Eshraghian , Member, IEEE, Melika Payvand , Member, IEEE, Elisa Donati , Member, IEEE, Bernabé Linares-Barranco , Fellow, IEEE, and Giacomo Indiveri , Senior Member, IEEE Abstract—The advent of dedicated Deep Learning (DL) accel- Index Terms—CMOS, deep neural networks, FPGA, healthcare, erators and neuromorphic processors has brought on new op- medical IoT, memristor, neuromorphic hardware, point-of-care, portunities for applying both Deep and Spiking Neural Network RRAM, spiking neural networks. (SNN) algorithms to healthcare and biomedical applications at the edge. This can facilitate the advancement of medical Internet I. INTRODUCTION of Things (IoT) systems and Point of Care (PoC) devices. In this RTIFICIAL intelligence is uniquely poised to cope with paper, we provide a tutorial describing how various technologies including emerging memristive devices, Field Programmable Gate the growing demands of the universal healthcare sys- Arrays (FPGAs), and Complementary Metal Oxide Semiconductor tem [1]. The healthcare industry is projected to reach over (CMOS) can be used to develop efficient DL accelerators 
TI - Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications
JF - IEEE Transactions on Biomedical Circuits and Systems
DO - 10.1109/tbcas.2020.3036081
DA - 2020-12-01
UR - https://www.deepdyve.com/lp/unpaywall/hardware-implementation-of-deep-network-accelerators-towards-fyF3UzSi3y
DP - DeepDyve
ER -