TY - GEN
T1 - TizBin
T2 - 40th IEEE International Conference on Computer Design, ICCD 2022
AU - Tabrizchi, Sepehr
AU - Angizi, Shaahin
AU - Roohi, Arman
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In the Artificial Intelligence of Things (AIoT) era, always-on intelligent and self-powered visual perception systems have gained considerable attention and are widely used. Thus, this paper proposes TizBin, a low-power processing in-sensor scheme with event and object detection capabilities to eliminate power costs of data conversion and transmission and enable data-intensive neural network tasks. Once the moving object is detected, TizBin architecture switches to the high-power object detection mode to capture the image. TizBin offers several unique features, such as analog convolutions enabling low-precision ternary weight neural networks (TWNN) to mitigate the overhead of analog buffer and analog-to-digital converters. Moreover, TizBin exploits non-volatile magnetic RAMs to store NN's weights, remarkably reducing static power consumption. Our circuit-to-application co-simulation results for TWNNs demonstrate minor accuracy degradation on various image datasets, while TizBin achieves a frame rate of 1000 and efficiency of ∼1.83 TOp/s/W.
AB - In the Artificial Intelligence of Things (AIoT) era, always-on intelligent and self-powered visual perception systems have gained considerable attention and are widely used. Thus, this paper proposes TizBin, a low-power processing in-sensor scheme with event and object detection capabilities to eliminate power costs of data conversion and transmission and enable data-intensive neural network tasks. Once the moving object is detected, TizBin architecture switches to the high-power object detection mode to capture the image. TizBin offers several unique features, such as analog convolutions enabling low-precision ternary weight neural networks (TWNN) to mitigate the overhead of analog buffer and analog-to-digital converters. Moreover, TizBin exploits non-volatile magnetic RAMs to store NN's weights, remarkably reducing static power consumption. Our circuit-to-application co-simulation results for TWNNs demonstrate minor accuracy degradation on various image datasets, while TizBin achieves a frame rate of 1000 and efficiency of ∼1.83 TOp/s/W.
KW - n/a
UR - http://www.scopus.com/inward/record.url?scp=85144387966&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85144387966&partnerID=8YFLogxK
U2 - 10.1109/ICCD56317.2022.00117
DO - 10.1109/ICCD56317.2022.00117
M3 - Conference contribution
AN - SCOPUS:85144387966
T3 - Proceedings - IEEE International Conference on Computer Design: VLSI in Computers and Processors
SP - 770
EP - 777
BT - Proceedings - 2022 IEEE 40th International Conference on Computer Design, ICCD 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 October 2022 through 26 October 2022
ER -