Name
Abstract | ||
|---|---|---|
This work presents an ultra-low-power classifier that can be integrated within energy-constrained bio-sensors to enable rapid analysis for continuous health monitoring. The in-sensor classifier saves significant transmission energy by extracting critical information locally to eliminate the need of transmitting raw data to centralized servers for remote signal processing. The convolutional-neural-network (CNN)-based classifier is built by using reconfigurable delay-locked loops (DLLs) to carry out classification algorithms with time-domain multiply-accumulate (MAC) operations. Pseudo sigmoid activation functions are realized by regenerative comparators that transform weighted timing to probabilities. The presented classifier achieves low-power consumption of 240.34 nW while performing up to 20 k operations per second. The proposed time-domain classifier reduces the energy to 36% of the previous works. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.vlsi.2020.03.002 | Integration |
Keywords | DocType | Volume |
Classifier,Machine learning,Smart sensors,Time-domain,Inner-products | Journal | 73 |
ISSN | Citations | PageRank |
0167-9260 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ethan Chen | 1 | 0 | 0.34 |
| Chen, V.H.-C. | 2 | 5 | 2.66 |