TY - GEN
T1 - System-level benchmark of synaptic device characteristics for neuro-inspired computing
AU - Chen, Pai Yu
AU - Peng, Xiaochen
AU - Yu, Shimeng
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Synaptic devices based on emerging non-volatile memory devices have been proposed to emulate analog synapses for neuro-inspired computing. However, the non-ideal device characteristics such as nonlinear and asymmetric weight increase/decrease, and finite on/off ratio, may adversely affect the learning accuracy at the system-level. In this paper, we present a device-circuit-algorithm co-simulation framework, i.e. NeuroSim, to systematically the metrics such as accuracy, area, latency and energy for online learning with synaptic devices. We surveyed a few representative synaptic devices in literature, and concluded that today's realistic devices are difficult to achieve accurate and fast learning. Finally, the targeted and ideal specifications for synaptic device engineering are proposed.
AB - Synaptic devices based on emerging non-volatile memory devices have been proposed to emulate analog synapses for neuro-inspired computing. However, the non-ideal device characteristics such as nonlinear and asymmetric weight increase/decrease, and finite on/off ratio, may adversely affect the learning accuracy at the system-level. In this paper, we present a device-circuit-algorithm co-simulation framework, i.e. NeuroSim, to systematically the metrics such as accuracy, area, latency and energy for online learning with synaptic devices. We surveyed a few representative synaptic devices in literature, and concluded that today's realistic devices are difficult to achieve accurate and fast learning. Finally, the targeted and ideal specifications for synaptic device engineering are proposed.
KW - neural network
KW - resistive memory
KW - synaptic device
UR - http://www.scopus.com/inward/record.url?scp=85047730993&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85047730993&partnerID=8YFLogxK
U2 - 10.1109/S3S.2017.8309197
DO - 10.1109/S3S.2017.8309197
M3 - Conference contribution
AN - SCOPUS:85047730993
T3 - 2017 IEEE SOI-3D-Subthreshold Microelectronics Unified Conference, S3S 2017
SP - 1
EP - 2
BT - 2017 IEEE SOI-3D-Subthreshold Microelectronics Unified Conference, S3S 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE SOI-3D-Subthreshold Microelectronics Unified Conference, S3S 2017
Y2 - 16 October 2017 through 18 October 2017
ER -