TY - GEN
T1 - Co-design of DNN model optimization for binary ReRAM array in-memory processing
AU - Guan, Yue
AU - Ohsawa, Takashi
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - In this work, a novel design of ReRAM neuromorphic system is proposed to process deep neural network (DNN) fully in array efficiently. A binary neural network model is constructed and optimized on MNIST dataset. The obtained model is simulated to be processed with the proposed ReRAM array. Co-design between hardware and network model optimization in software is analyzed to achieve feasible hardware design and generalizable model. Deployed with such co-design model, ReRAM array processes DNN with high robustness against fabrication fluctuation.
AB - In this work, a novel design of ReRAM neuromorphic system is proposed to process deep neural network (DNN) fully in array efficiently. A binary neural network model is constructed and optimized on MNIST dataset. The obtained model is simulated to be processed with the proposed ReRAM array. Co-design between hardware and network model optimization in software is analyzed to achieve feasible hardware design and generalizable model. Deployed with such co-design model, ReRAM array processes DNN with high robustness against fabrication fluctuation.
KW - binary neural network
KW - fabrication fluctuation
KW - neuromorphic ReRAM
UR - http://www.scopus.com/inward/record.url?scp=85068349191&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068349191&partnerID=8YFLogxK
U2 - 10.1109/IMW.2019.8739722
DO - 10.1109/IMW.2019.8739722
M3 - Conference contribution
AN - SCOPUS:85068349191
T3 - 2019 IEEE 11th International Memory Workshop, IMW 2019
BT - 2019 IEEE 11th International Memory Workshop, IMW 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 11th IEEE International Memory Workshop, IMW 2019
Y2 - 12 May 2019 through 15 May 2019
ER -