TY - GEN
T1 - In-sensor Computing Devices for Bio-inspired Vision Sensors
AU - Liao, Fuyou
AU - Chai, Yang
N1 - Funding Information:
This work was supported by China Postdoctoral Science Foundation (2021M692221), Research rG ant of Council of Hong Kong (PolyU 15205619), the Innovation and Technology Fund (ITS/047/20), and the Hong Kong Polytechnic University (1-EZ 1T and SB4C).
Publisher Copyright:
© 2022 IEEE.
PY - 2022/6
Y1 - 2022/6
N2 - The rapid development of Internet of Things generates abundant data, which demands a new computing paradigm to efficiently process these data at sensory terminals. Vision is the most typical sensor-data-intensive application. Biological visual systems have considerable advantages in terms of energy efficiency and multiple functionalities. The existing artificial vision system based on conventional image sensors, memory, and processing units has complex circuitry, high power consumption, and limited perception range. We design and demonstrate optoelectronic devices for bio-inspired visual sensors, which enable image sensing, neuromorphic visual pre-processing, and effective adaptation to a wide range of light-intensity.
AB - The rapid development of Internet of Things generates abundant data, which demands a new computing paradigm to efficiently process these data at sensory terminals. Vision is the most typical sensor-data-intensive application. Biological visual systems have considerable advantages in terms of energy efficiency and multiple functionalities. The existing artificial vision system based on conventional image sensors, memory, and processing units has complex circuitry, high power consumption, and limited perception range. We design and demonstrate optoelectronic devices for bio-inspired visual sensors, which enable image sensing, neuromorphic visual pre-processing, and effective adaptation to a wide range of light-intensity.
KW - bio-inspired vision sensors
KW - in-sensor computing
KW - light adaptation
KW - optoelectronic memory
UR - http://www.scopus.com/inward/record.url?scp=85133953316&partnerID=8YFLogxK
U2 - 10.1109/EDTM53872.2022.9798059
DO - 10.1109/EDTM53872.2022.9798059
M3 - Conference article published in proceeding or book
AN - SCOPUS:85133953316
T3 - 6th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2022
SP - 307
EP - 309
BT - 6th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE Electron Devices Technology and Manufacturing Conference, EDTM 2022
Y2 - 6 March 2022 through 9 March 2022
ER -
