TY - GEN
T1 - Design of Switched-Current Based Low-Power PIM Vision System for IoT Applications
AU - Liu, Zheyu
AU - Fan, Zichen
AU - Wei, Qi
AU - Wu, Xing
AU - Qiao, Fei
AU - Jin, Ping
AU - Liu, Xin Jun
AU - Liu, Chengliang
AU - Yang, Huazhong
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Neural networks(NN) is becoming dominant in machine learning field for its excellent performance in classification, recognition and so on. However, the huge computation and memory overhead make it hard to implement NN algorithms on the existing platforms with real-time and energy-efficient performance. In this work, a low-power processing-in-memory (PIM) vision system for accelerate binary weight networks is proposed. This architecture utilizes PIM and features an energy-efficient switched current (SI) neuron, employing a network with binary weight and 9-bit activation. Simulation result shows the design occupies 5.82mm2 in SMIC 180nm CMOS technology, which consumes 1.45mW from 1.8V supplies. Our system outperforms the state-of-the-art designs in terms of power consumption and achieves energy efficiency up to 28.25TOPS/W.
AB - Neural networks(NN) is becoming dominant in machine learning field for its excellent performance in classification, recognition and so on. However, the huge computation and memory overhead make it hard to implement NN algorithms on the existing platforms with real-time and energy-efficient performance. In this work, a low-power processing-in-memory (PIM) vision system for accelerate binary weight networks is proposed. This architecture utilizes PIM and features an energy-efficient switched current (SI) neuron, employing a network with binary weight and 9-bit activation. Simulation result shows the design occupies 5.82mm2 in SMIC 180nm CMOS technology, which consumes 1.45mW from 1.8V supplies. Our system outperforms the state-of-the-art designs in terms of power consumption and achieves energy efficiency up to 28.25TOPS/W.
KW - near sensor processing
KW - neural networks
KW - processing in memory
KW - switched current circuit
KW - vision system
UR - https://www.scopus.com/pages/publications/85072956181
U2 - 10.1109/ISVLSI.2019.00041
DO - 10.1109/ISVLSI.2019.00041
M3 - 会议稿件
AN - SCOPUS:85072956181
T3 - Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI
SP - 181
EP - 186
BT - Proceedings - 2019 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019
PB - IEEE Computer Society
T2 - 18th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019
Y2 - 15 July 2019 through 17 July 2019
ER -
