LSTMs for Keyword Spotting with ReRAM-Based Compute-In-Memory Architectures-Reference-Cited by-同舟云学术

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LSTMs for Keyword Spotting with ReRAM-Based Compute-In-Memory Architectures

Published:2021-05 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE International Symposium on Circuits and Systems (ISCAS)
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Author:

Schaefer Clemens JS,Horeni Mark,Taheri Pooria,Joshi Siddharth

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9401028/9401051/09401295.pdf?arnumber=9401295

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Study of Sample Size Requirement and Effectiveness of Mel-Scaled Features for Small-Footprint Keyword Spotting in a Limited Dataset Environment;2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS);2023-06-19

2. A Ternary Weight Mapping and Charge-mode Readout Scheme for Energy Efficient FeRAM Crossbar Compute-in-Memory System;2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS);2023-06-11

3. A FeFET-Based ADC Offset Robust Compute-In-Memory Architecture for Streaming Keyword Spotting (KWS);IEEE Transactions on Emerging Topics in Computing;2023

4. A surrogate gradient spiking baseline for speech command recognition;Frontiers in Neuroscience;2022-08-22

5. Analog LSTM for Keyword Spotting;2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS);2022-06-13

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