TY - GEN
T1 - Processing-In-Memory Acceleration of Convolutional Neural Networks for Energy-Effciency, and Power-Intermittency Resilience
AU - Roohi, Arman
AU - Angizi, Shaahin
AU - Fan, Deliang
AU - Demara, Ronald F.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/4/23
Y1 - 2019/4/23
N2 - Herein, a bit-wise Convolutional Neural Network (CNN) in-memory accelerator is implemented using Spin-Orbit Torque Magnetic Random Access Memory (SOT-MRAM) computational sub-arrays. It utilizes a novel AND-Accumulation method capable of significantly-reduced energy consumption within convolutional layers and performs various low bitwidth CNN inference operations entirely within MRAM. Power-intermittence resiliency is also enhanced by retaining the partial state information needed to maintain computational forward-progress, which is advantageous for battery-less IoT nodes. Simulation results indicate ∼ 5.4× higher energy-efficiency and 9× speedup over ReRAM-based acceleration, or roughly ∼ 9.7× higher energy-efficiency and 13.5× speedup over recent CMOS-only approaches, while maintaining inference accuracy comparable to baseline designs.
AB - Herein, a bit-wise Convolutional Neural Network (CNN) in-memory accelerator is implemented using Spin-Orbit Torque Magnetic Random Access Memory (SOT-MRAM) computational sub-arrays. It utilizes a novel AND-Accumulation method capable of significantly-reduced energy consumption within convolutional layers and performs various low bitwidth CNN inference operations entirely within MRAM. Power-intermittence resiliency is also enhanced by retaining the partial state information needed to maintain computational forward-progress, which is advantageous for battery-less IoT nodes. Simulation results indicate ∼ 5.4× higher energy-efficiency and 9× speedup over ReRAM-based acceleration, or roughly ∼ 9.7× higher energy-efficiency and 13.5× speedup over recent CMOS-only approaches, while maintaining inference accuracy comparable to baseline designs.
UR - http://www.scopus.com/inward/record.url?scp=85065157455&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85065157455&partnerID=8YFLogxK
U2 - 10.1109/ISQED.2019.8697572
DO - 10.1109/ISQED.2019.8697572
M3 - Conference contribution
AN - SCOPUS:85065157455
T3 - Proceedings - International Symposium on Quality Electronic Design, ISQED
SP - 8
EP - 13
BT - Proceedings of the 20th International Symposium on Quality Electronic Design, ISQED 2019
PB - IEEE Computer Society
T2 - 20th International Symposium on Quality Electronic Design, ISQED 2019
Y2 - 6 March 2019 through 7 March 2019
ER -