The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
저수지 컴퓨팅(RC)은 계산 비용이 저렴한 훈련 프로세스와 단순성으로 인해 기계 학습 모델의 매력적인 대안입니다. 이번 작업에서 우리는 제안합니다. 앙상블블룸CA, 셀룰러 오토마타(CA)와 앙상블 블룸 필터를 활용하여 RC 시스템을 구성합니다. 대부분의 기존 RC 시스템과 달리 앙상블블룸CA 모든 부동 소수점 계산과 정수 곱셈을 제거합니다. 앙상블블룸CA CA는 이진 연산만 사용하여 구현할 수 있어 에너지 효율적이기 때문에 RC 시스템의 저장소로 CA를 채택합니다. CA가 생성한 풍부한 패턴 역학은 원래 입력을 고차원 공간에 매핑하고 분류기에 더 많은 기능을 제공할 수 있습니다. 앙상블 블룸 필터를 분류자로 활용하면 저장소가 제공하는 특징을 효과적으로 기억할 수 있습니다. 우리의 실험에서는 Bloom 필터에 앙상블 메커니즘을 적용하면 추론 단계에서 메모리 비용이 크게 감소하는 것으로 나타났습니다. 비교하면 블룸 위사드, 최첨단 참고 작품 중 하나인 앙상블블룸CA 모델은 동일한 정확도를 유지하면서 메모리 비용을 43배 절감합니다. 우리의 하드웨어 구현은 또한 다음을 입증했습니다. 앙상블블룸CA 면적과 전력이 각각 23배 및 8.5배 이상 감소했습니다.
Dehua LIANG
Osaka University
Jun SHIOMI
Osaka University
Noriyuki MIURA
Osaka University
Masanori HASHIMOTO
Kyoto University
Hiromitsu AWANO
Kyoto University
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
부
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
부
@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},}
부
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 -