Monadic Pavlovian associative learning in a backpropagation-free photonic network
| Autor: | James Y. S. Tan, Zengguang Cheng, Johannes Feldmann, Xuan Li, Nathan Youngblood, Utku E. Ali, C. David Wright, Wolfram H. P. Pernice, Harish Bhaskaran |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Neural and Evolutionary Computing FOS: Physical sciences Physics - Applied Physics Neural and Evolutionary Computing (cs.NE) Applied Physics (physics.app-ph) Atomic and Molecular Physics and Optics Physics - Optics Optics (physics.optics) Electronic Optical and Magnetic Materials |
| Popis: | Over a century ago, Ivan P. Pavlov, in a classic experiment, demonstrated how dogs can learn to associate a ringing bell with food, thereby causing a ring to result in salivation. Today, it is rare to find the use of Pavlovian type associative learning for artificial intelligence (AI) applications even though other learning concepts, in particular backpropagation on artificial neural networks (ANNs) have flourished. However, training using the backpropagation method on 'conventional' ANNs, especially in the form of modern deep neural networks (DNNs), is computationally and energy intensive. Here we experimentally demonstrate a form of backpropagation-free learning using a single (or monadic) associative hardware element. We realize this on an integrated photonic platform using phase-change materials combined with on-chip cascaded directional couplers. We then develop a scaled-up circuit network using our monadic Pavlovian photonic hardware that delivers a distinct machine-learning framework based on single-element associations and, importantly, using backpropagation-free architectures to address general learning tasks. Our approach reduces the computational burden imposed by learning in conventional neural network approaches, thereby increasing speed, whilst also offering higher bandwidth inherent to our photonic implementation. Comment: 24 pages, 5 figures |
| Databáze: | OpenAIRE |
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