Name
Title | ||
|---|---|---|
14.1 A 510nW 0.41V Low-Memory Low-Computation Keyword-Spotting Chip Using Serial FFT-Based MFCC and Binarized Depthwise Separable Convolutional Neural Network in 28nm CMOS. |
Abstract | ||
|---|---|---|
Ultra-low power is a strong requirement for always-on speech interfaces in wearable and mobile devices, such as Voice Activity Detection (VAD) and Keyword Spotting (KWS) [1]–[5]. A KWS system is used to detect specific wake-up words by speakers and has to be always on. Previous ASICs for KWS lack energy-efficient implementations having power $ . For example, deep neural network (DNN)-based KWS [1] has a large on-chip weight memory of 270KB and consumes $288\mu \mathrm{W}$ . A binarized convolutional neural network (CNN) used 52KB of SRAM, $141\mu \mathrm{W}$ wakeup power at 2.5MHz, 0.57V [2]. An LSTM-based SoC used 105KB of SRAM and reduced power to $16.11\mu\mathrm{W}$ for KWS with 90.8% accuracy on the Google Speech Command Dataset (GSCD) [3]. Laika reduced power to $5\mu \mathrm{W}$ [4], not including the Mel Frequency Cepstrum Coefficient (MFCC) circuit. High compute and memory requirements have prevented always-on KWS chips from operating in the $\mathrm{sub}-\mu \mathrm{W}$ range. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ISSCC19947.2020.9063000 | ISSCC |
DocType | Citations | PageRank |
Conference | 2 | 0.45 |
References | Authors | |
0 | 9 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiwei Shan | 1 | 2 | 0.45 |
| Minhao Yang | 2 | 87 | 9.02 |
| Jiaming Xu | 3 | 284 | 35.34 |
| Yi-Cheng Lu | 4 | 5 | 2.23 |
| Shuai Zhang | 5 | 37 | 11.44 |
| Tao Wang | 6 | 2 | 1.13 |
| Jun Yang | 7 | 147 | 36.54 |
| Longxing Shi | 8 | 116 | 39.08 |
| Mingoo Seok | 9 | 11 | 6.77 |