Name
Title | ||
|---|---|---|
Developing a Miniature Energy-Harvesting-Powered Edge Device with Multi-Exit Neural Network |
Abstract | ||
|---|---|---|
This paper describes a miniature edge device that performs neural network inference with different exit options depending on available energy. In addition to the main-exit path, it provides an alternative, early-exit path that requires less computation and thus increase the number of inference operations for given energy. To compensate its degraded accuracy, the proposed device provides entropy as a confidence level for the early exit. The network is implemented with a custom low-power 180 nm CMOS processor chip and a 90 nm embedded flash memory chip and tested by images from CIFAR-10 dataset. The measurement results show the proposed neural network reduces processing time and thus energy consumption by 41.3% compared with the main-exit only method while sacrificing its accuracy from 69.5% to 66.0%. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ISCAS51556.2021.9401799 | 2021 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS (ISCAS) |
Keywords | DocType | ISSN |
Energy harvesting, neural network, multi-exit, miniature system | Conference | 0271-4302 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuyang Li | 1 | 0 | 2.37 |
| Yawen Wu | 2 | 1 | 3.73 |
| Xincheng Zhang | 3 | 0 | 0.34 |
| Ehab Hamed | 4 | 0 | 0.34 |
| Jingtong Hu | 5 | 963 | 76.16 |
| Inhee Lee | 6 | 275 | 33.89 |