BloomCA: A Memory Efficient Reservoir Computing Hardware Implementation Using Cellular Automata and Ensemble Bloom Filter
Dehua Liang1,a, Masanori Hashimoto1,b and Hiromitsu Awano2
1Osaka University Osaka, Japan
ad-liang@ist.osaka-u.ac.jp
bhasimoto@ist.osaka-u.ac.jp
2Kyoto University Kyoto, Japan
awano@i.kyoto-u.ac.jp
ABSTRACT
In this work, we propose a BloomCA which utilizes cellular automata (CA) and ensemble Bloom filter to organize an RC system by using only binary operations, which is suitable for hardware implementation. The rich pattern dynamics created by CA can map the input into high-dimensional space and provide more features for the classifier. Utilizing the ensemble Bloom filter as the classifier, the features can be memorized effectively. Our experiment reveals that applying the ensemble mechanism to Bloom filter endues a significant reduction in inference memory cost. Comparing with the state-of-the-art reference, the BloomCA achieves a 43× reduction for memory cost without hurting the accuracy. Our hardware implementation also demonstrates that BloomCA achieves over 21× and 43.64% reduction in area and power, respectively.
Keywords: BloomCA, Reservoir Computing, Cellular Automata, Ensemble Bloom Filter.
