摘要
In the past few years, the demand for intelligence of IoT front-end devices has dramatically increased. However, such devices face challenges of limited on-chip resources and strict power or energy constraints. Recent progress in binarized neural networks has provided promising solutions for front-end processing system to conduct simple detection and classification tasks by making trade-offs between the processing quality and the computation complexity. In this paper, we propose a mixed-signal perception chip, in which an ADC-free 32x32 image sensor and a BNN processing array are directly integrated with a 180nm standard CMOS process. Taking advantage of the ADC-free processing architecture, the whole processing system only consumes 1.8mW power, while providing up to 545.4 GOPS/W energy efficiency. The implementation performance and energy efficiency are comparable with the state-of-the-art designs in much more advanced CMOS technologies. This work provides a promising alternative for low-power IoT intelligent applications.
| 源语言 | 英语 |
|---|---|
| 主期刊名 | Proceedings - 2019 IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019 |
| 出版商 | IEEE Computer Society |
| 页 | 447-452 |
| 页数 | 6 |
| ISBN(电子版) | 9781538670996 |
| DOI | |
| 出版状态 | 已出版 - 7月 2019 |
| 活动 | 18th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019 - Miami, 美国 期限: 15 7月 2019 → 17 7月 2019 |
出版系列
| 姓名 | Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI |
|---|---|
| 卷 | 2019-July |
| ISSN(印刷版) | 2159-3469 |
| ISSN(电子版) | 2159-3477 |
会议
| 会议 | 18th IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2019 |
|---|---|
| 国家/地区 | 美国 |
| 市 | Miami |
| 时期 | 15/07/19 → 17/07/19 |
联合国可持续发展目标
此成果有助于实现下列可持续发展目标:
-
可持续发展目标 7 经济适用的清洁能源
指纹
探究 'A 1.8mW Perception Chip with Near-Sensor Processing Scheme for Low-Power AIoT Applications' 的科研主题。它们共同构成独一无二的指纹。引用此
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver