IEICE TRANSACTIONS on Information
A Hardware Efficient Reservoir Computing System Using Cellular Automata and Ensemble Bloom Filter
Summary :
Reservoir computing (RC) is an attractive alternative to machine learning models owing to its computationally inexpensive training process and simplicity. In this work, we propose EnsembleBloomCA, which utilizes cellular automata (CA) and an ensemble Bloom filter to organize an RC system. In contrast to most existing RC systems, EnsembleBloomCA eliminates all floating-point calculation and integer multiplication. EnsembleBloomCA adopts CA as the reservoir in the RC system because it can be implemented using only binary operations and is thus energy efficient. The rich pattern dynamics created by CA can map the original input into a high-dimensional space and provide more features for the classifier. Utilizing an ensemble Bloom filter as the classifier, the features provided by the reservoir can be effectively memorized. Our experiment revealed that applying the ensemble mechanism to the Bloom filter resulted in a significant reduction in memory cost during the inference phase. In comparison with Bloom WiSARD, one of the state-of-the-art reference work, the EnsembleBloomCA model achieves a 43× reduction in memory cost while maintaining the same accuracy. Our hardware implementation also demonstrated that EnsembleBloomCA achieved over 23× and 8.5× reductions in area and power, respectively.
- Publication
- IEICE TRANSACTIONS on Information Vol.E105-D No.7 pp.1273-1282
- Publication Date
- 2022/07/01
- Publicized
- 2022/04/08
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2021EDP7203
- Type of Manuscript
- PAPER
- Category
- Computer System
Authors
Dehua LIANG
Osaka University
Jun SHIOMI
Osaka University
Noriyuki MIURA
Osaka University
Masanori HASHIMOTO
Kyoto University
Hiromitsu AWANO
Kyoto University
Keyword
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Dehua LIANG, Jun SHIOMI, Noriyuki MIURA, Masanori HASHIMOTO, Hiromitsu AWANO, "A Hardware Efficient Reservoir Computing System Using Cellular Automata and Ensemble Bloom Filter" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 7, pp. 1273-1282, July 2022, doi: 10.1587/transinf.2021EDP7203.
Abstract: Reservoir computing (RC) is an attractive alternative to machine learning models owing to its computationally inexpensive training process and simplicity. In this work, we propose EnsembleBloomCA, which utilizes cellular automata (CA) and an ensemble Bloom filter to organize an RC system. In contrast to most existing RC systems, EnsembleBloomCA eliminates all floating-point calculation and integer multiplication. EnsembleBloomCA adopts CA as the reservoir in the RC system because it can be implemented using only binary operations and is thus energy efficient. The rich pattern dynamics created by CA can map the original input into a high-dimensional space and provide more features for the classifier. Utilizing an ensemble Bloom filter as the classifier, the features provided by the reservoir can be effectively memorized. Our experiment revealed that applying the ensemble mechanism to the Bloom filter resulted in a significant reduction in memory cost during the inference phase. In comparison with Bloom WiSARD, one of the state-of-the-art reference work, the EnsembleBloomCA model achieves a 43× reduction in memory cost while maintaining the same accuracy. Our hardware implementation also demonstrated that EnsembleBloomCA achieved over 23× and 8.5× reductions in area and power, respectively.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDP7203/_p
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@ARTICLE{e105-d_7_1273,
author={Dehua LIANG, Jun SHIOMI, Noriyuki MIURA, Masanori HASHIMOTO, Hiromitsu AWANO, },
journal={IEICE TRANSACTIONS on Information},
title={A Hardware Efficient Reservoir Computing System Using Cellular Automata and Ensemble Bloom Filter},
year={2022},
volume={E105-D},
number={7},
pages={1273-1282},
abstract={Reservoir computing (RC) is an attractive alternative to machine learning models owing to its computationally inexpensive training process and simplicity. In this work, we propose EnsembleBloomCA, which utilizes cellular automata (CA) and an ensemble Bloom filter to organize an RC system. In contrast to most existing RC systems, EnsembleBloomCA eliminates all floating-point calculation and integer multiplication. EnsembleBloomCA adopts CA as the reservoir in the RC system because it can be implemented using only binary operations and is thus energy efficient. The rich pattern dynamics created by CA can map the original input into a high-dimensional space and provide more features for the classifier. Utilizing an ensemble Bloom filter as the classifier, the features provided by the reservoir can be effectively memorized. Our experiment revealed that applying the ensemble mechanism to the Bloom filter resulted in a significant reduction in memory cost during the inference phase. In comparison with Bloom WiSARD, one of the state-of-the-art reference work, the EnsembleBloomCA model achieves a 43× reduction in memory cost while maintaining the same accuracy. Our hardware implementation also demonstrated that EnsembleBloomCA achieved over 23× and 8.5× reductions in area and power, respectively.},
keywords={},
doi={10.1587/transinf.2021EDP7203},
ISSN={1745-1361},
month={July},}
Copy
TY - JOUR
TI - A Hardware Efficient Reservoir Computing System Using Cellular Automata and Ensemble Bloom Filter
T2 - IEICE TRANSACTIONS on Information
SP - 1273
EP - 1282
AU - Dehua LIANG
AU - Jun SHIOMI
AU - Noriyuki MIURA
AU - Masanori HASHIMOTO
AU - Hiromitsu AWANO
PY - 2022
DO - 10.1587/transinf.2021EDP7203
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2022
AB - Reservoir computing (RC) is an attractive alternative to machine learning models owing to its computationally inexpensive training process and simplicity. In this work, we propose EnsembleBloomCA, which utilizes cellular automata (CA) and an ensemble Bloom filter to organize an RC system. In contrast to most existing RC systems, EnsembleBloomCA eliminates all floating-point calculation and integer multiplication. EnsembleBloomCA adopts CA as the reservoir in the RC system because it can be implemented using only binary operations and is thus energy efficient. The rich pattern dynamics created by CA can map the original input into a high-dimensional space and provide more features for the classifier. Utilizing an ensemble Bloom filter as the classifier, the features provided by the reservoir can be effectively memorized. Our experiment revealed that applying the ensemble mechanism to the Bloom filter resulted in a significant reduction in memory cost during the inference phase. In comparison with Bloom WiSARD, one of the state-of-the-art reference work, the EnsembleBloomCA model achieves a 43× reduction in memory cost while maintaining the same accuracy. Our hardware implementation also demonstrated that EnsembleBloomCA achieved over 23× and 8.5× reductions in area and power, respectively.
ER -
English
