A $1.5\mu\mathrm{W}$ End-to-End Keyword Spotting SoC with Content-Adaptive Frame Sub-Sampling and Fast-Settling Analog Frontend
Author:
Affiliation:
1. University of Michigan,Ann Arbor,MI
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10067248/10067251/10067808.pdf?arnumber=10067808
Reference6 articles.
1. Skip RNN: Learning to Skip State Updates in Recurrent Neural Networks;campos;ICLRE,2018
2. 18\mu W SoC for Near-Microphone Keyword Spotting and Speaker Verification;giraldo;IEEE Symp VLSI Circuits,0
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4. A 0.19x0.17 mm2 Wireless Neural Recording IC for Motor Prediction with Near-Infrared-Based Power and Data Telemetry;lim;ISSCC,2020
5. 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
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1. A 1.5-μW Fully-Integrated Keyword Spotting SoC in 28-nm CMOS With Skip-RNN and Fast-Settling Analog Frontend for Adaptive Frame Skipping;IEEE Journal of Solid-State Circuits;2024-01
2. A183.4-nJ/Inference 152.8-μW 35-Voice Commands Recognition Wired-Logic Processor Using Algorithm-Circuit Co-Optimization Technique;IEEE Solid-State Circuits Letters;2024
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