Monadic Pavlovian associative learning in a backpropagation-free photonic network
This work offers a potentially more energy-efficient and faster alternative for machine learning by avoiding backpropagation, which could benefit applications where computational resources are limited.
This paper demonstrates a backpropagation-free learning method using a single associative hardware element on an integrated photonic platform. The authors developed a scaled-up circuit network using this hardware to address general learning tasks, aiming to reduce computational burden and increase speed compared to conventional neural networks.
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.