TY - GEN
T1 - Design insights of non-volatile processors and accelerators in energy harvesting systems
AU - Qiu, Keni
AU - Zhao, Mengying
AU - Jia, Zhenge
AU - Hu, Jingtong
AU - Xue, Chun Jason
AU - Ma, Kaisheng
AU - Li, Xueqing
AU - Liu, Yongpan
AU - Narayanan, Vijaykrishnan
N1 - Funding Information:
This work was supported in part by NSF #1822923, NSFC #61872251 and Beijing Advanced Innovation Center for Imaging Technology.
PY - 2020/9/7
Y1 - 2020/9/7
N2 - There is growing interest in deploying energy harvesting processors and accelerators in Internet of Things (IoT). Energy harvesting harnesses the energy scavenged from the environment to power a system. Although it has many advantages over battery-operated systems such as lightweight, compact size, and no necessity of recharging and maintenance, it may suffer frequently power-down and a fluctuating power supply even with power on. Non-volatile processor (NVP) is a promising architecture for effective computing in energy harvesting scenarios. Recently, non-volatile accelerators (NVA) have been proposed to perform computations of deep learning algorithms. In this paper, we overview the recent studies of NVP and NVA across the layers of hardware, architecture, software and their co-design. Especially, we present the design insights of how the state-of-the-art works adapt their specific designs to the intermittent and fluctuating power conditions with the energy harvesting technology. Finally, we discuss recent trends using NVP and NVA in energy harvesting scenarios.
AB - There is growing interest in deploying energy harvesting processors and accelerators in Internet of Things (IoT). Energy harvesting harnesses the energy scavenged from the environment to power a system. Although it has many advantages over battery-operated systems such as lightweight, compact size, and no necessity of recharging and maintenance, it may suffer frequently power-down and a fluctuating power supply even with power on. Non-volatile processor (NVP) is a promising architecture for effective computing in energy harvesting scenarios. Recently, non-volatile accelerators (NVA) have been proposed to perform computations of deep learning algorithms. In this paper, we overview the recent studies of NVP and NVA across the layers of hardware, architecture, software and their co-design. Especially, we present the design insights of how the state-of-the-art works adapt their specific designs to the intermittent and fluctuating power conditions with the energy harvesting technology. Finally, we discuss recent trends using NVP and NVA in energy harvesting scenarios.
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U2 - 10.1145/3386263.3407596
DO - 10.1145/3386263.3407596
M3 - Conference contribution
AN - SCOPUS:85091312222
T3 - Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI
SP - 369
EP - 374
BT - GLSVLSI 2020 - Proceedings of the 2020 Great Lakes Symposium on VLSI
PB - Association for Computing Machinery
T2 - 30th Great Lakes Symposium on VLSI, GLSVLSI 2020
Y2 - 7 September 2020 through 9 September 2020
ER -