Markov Chain Hebbian Learning Algorithm With Ternary Synaptic Unitsopen access
- Authors
- Kim, Guhyun; Kornijcuk, Vladimir; Kim, Dohun; Kim, Inho; Kim, Jaewook; Woo, Hyo Cheon; Kim, Jihun; Hwang, Cheol Seong; Jeong, Doo Seok
- Issue Date
- Jan-2019
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Greedy edge-wise training; Hebbian learning; Markov chain; mental arithmetic; prime factorization; supervised learning; ternary unit
- Citation
- IEEE ACCESS, v.7, pp.10208 - 10223
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE ACCESS
- Volume
- 7
- Start Page
- 10208
- End Page
- 10223
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/15138
- DOI
- 10.1109/ACCESS.2018.2890543
- ISSN
- 2169-3536
- Abstract
- In spite of remarkable progress in machine learning techniques, the state-of-the-art machine learning algorithms often keep machines from real-time learning (online learning) due, in part, to computational complexity in parameter optimization. As an alternative, a learning algorithm to train a memory in real time is proposed, named the Markov chain Hebbian learning algorithm. The algorithm pursues efficient use in memory during training in that: 1) the weight matrix has ternary elements (-1, 0, 1) and 2) each update follows a Markov chain-the upcoming update does not need past weight values. The algorithm was verified by two proof-of-concept tasks: image (MNIST and CIFAR-10 datasets) recognition and multiplication table memorization. Particularly, the latter bases multiplication arithmetic on memory, which may be analogous to humans' mental arithmetic. The memory-based multiplication arithmetic feasibly offers the basis of factorization, supporting novel insight into memory-based arithmetic.
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