A High-performance RNS LSTM block
Author:
Affiliation:
1. Khalifa University,Electrical Engineering and Computer Science Department,Abu Dhabi,UAE
2. University of Patras,Electrical and Computer Engineering Department,Patras,Greece
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9937201/9937203/09937633.pdf?arnumber=9937633
Reference23 articles.
1. Applying the residue number system to network inference;abdelhamid;arXiv preprint arXiv 1712 04614,2017
2. LSTM: A Search Space Odyssey
3. Elementary Functions
4. Fast Scaling in the Residue Number System
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1. An end-to-end RNS CNN Accelerator;2024 IEEE 6th International Conference on AI Circuits and Systems (AICAS);2024-04-22
2. Improving Residue-Level Sparsity in RNS-based Neural Network Hardware Accelerators via Regularization;2023 IEEE 30th Symposium on Computer Arithmetic (ARITH);2023-09-04
3. RNS for DNN Architectures;Synthesis Lectures on Engineering, Science, and Technology;2023-09-02
4. On the modulo 2 +1 addition and subtraction for weighted operands;Microprocessors and Microsystems;2023-09
5. Support vector machines implementation over integers modulo-M and Residue Number System;DYNA;2023-05-25
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