Neural Network Training Acceleration With RRAM-Based Hybrid Synapses
Hardware neural network (HNN) based on analog synapse array excels in accelerating parallel computations. To implement an energy-efficient HNN with high accuracy, high-precision synaptic devices and fully-parallel array operations are essential. However, existing resistive memory (RRAM) devices can...
Main Authors: | Wooseok Choi, Myonghoon Kwak, Seyoung Kim, Hyunsang Hwang |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2021-06-01
|
Series: | Frontiers in Neuroscience |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2021.690418/full |
Similar Items
-
Cases Study of Inputs Split Based Calibration Method for RRAM Crossbar
by: Sheng-Yang Sun, et al.
Published: (2019-01-01) -
Efficient and Optimized Methods for Alleviating the Impacts of IR-Drop and Fault in RRAM Based Neural Computing Systems
by: Chenglong Huang, et al.
Published: (2021-01-01) -
HFNet: A CNN Architecture Co-designed for Neuromorphic Hardware With a Crossbar Array of Synapses
by: Roshan Gopalakrishnan, et al.
Published: (2020-10-01) -
Selected Bit-Line Current PUF: Implementation of Hardware Security Primitive Based on a Memristor Crossbar Array
by: Dayoung Kim, et al.
Published: (2021-01-01) -
Weighted Synapses Without Carry Operations for RRAM-Based Neuromorphic Systems
by: Yan Liao, et al.
Published: (2018-03-01)