TY - GEN
T1 - Low energy domain wall memory based convolution neural network design with optimizing MAC architecture
AU - Kim, Jooyoon
AU - Jang, Yunho
AU - Kim, Taehwan
AU - Park, Jongsun
N1 - Funding Information:
This work was supported in part by the National Research Foundation of Korea grant funded by the Korea government (NRF-2015M3D1A1070465), in part by National R&D Program through the National Research Foundation of Korea funded by Ministry of Science and ICT (NRF- 2020M3F3A2A01082591), and in part by the National Research Foundation of Korea grant funded by the Korea government (NRF- 2020R1A2C3014820).
Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - Running a convolutional neural network (CNN) algorithm using dedicated integrated circuits (ICs) on real-time portable applications is mainly restricted by slow performance and large power consumption. The power and delay are mainly due to external memory access, which incurs considerable energy consumption and bandwidth issues. In this paper, we propose an efficient convolution layer design using domain wall memory (DWM) for eliminating external memory access in image sensor embedded applications. A low energy access scheme using tag is employed to further reduce power consumption. The experimental results show that the proposed CNN architecture achieves 11.2% memory energy savings and 21.8% of MAC operation reduction compared to conventional architecture.
AB - Running a convolutional neural network (CNN) algorithm using dedicated integrated circuits (ICs) on real-time portable applications is mainly restricted by slow performance and large power consumption. The power and delay are mainly due to external memory access, which incurs considerable energy consumption and bandwidth issues. In this paper, we propose an efficient convolution layer design using domain wall memory (DWM) for eliminating external memory access in image sensor embedded applications. A low energy access scheme using tag is employed to further reduce power consumption. The experimental results show that the proposed CNN architecture achieves 11.2% memory energy savings and 21.8% of MAC operation reduction compared to conventional architecture.
KW - Convolution neural network
KW - Domain wall memory
UR - http://www.scopus.com/inward/record.url?scp=85108989676&partnerID=8YFLogxK
U2 - 10.1109/ISCAS51556.2021.9401269
DO - 10.1109/ISCAS51556.2021.9401269
M3 - Conference contribution
AN - SCOPUS:85108989676
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
BT - 2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 53rd IEEE International Symposium on Circuits and Systems, ISCAS 2021
Y2 - 22 May 2021 through 28 May 2021
ER -
