Leveraging Sparsity with Spiking Recurrent Neural Networks for Energy-Efficient Keyword Spotting-Reference-Cited by-同舟云学术

Leveraging Sparsity with Spiking Recurrent Neural Networks for Energy-Efficient Keyword Spotting

Published:2023-06-04 Issue: Volume: Page:
ISSN:
Container-title:ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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Author:

Dampfhoffer Manon¹,Mesquida Thomas²,Hardy Emmanuel³,Valentian Alexandre²,Anghel Lorena¹

Affiliation:

1. Univ. Grenoble Alpes,Cea, Cnrs, Grenoble Inp, Inac-Spintec,Grenoble,France,38000

2. Univ. Grenoble Alpes,Cea, List,Grenoble,France,F-38000

3. Univ. Grenoble Alpes,Cea, Leti,Grenoble,France,F-38000

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10094559/10094560/10097174.pdf?arnumber=10097174

Reference18 articles.

1. Investigating Current-Based and Gating Approaches for Accurate and Energy-Efficient Spiking Recurrent Neural Networks

2. A 23?w solar-powered keyword-spotting asic with ring-oscillator-based time-domain feature extraction;kim;2022 IEEE Int Solid-State Circuits Conf (ISSCC),2022

3. SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition

4. Are SNNs really more energy-efficient than ANNs? An in-depth hardware-aware study;dampfhoffer;IEEE Transactions on Emerging Topics in Computational Intelligence,2022

5. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

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