Associative learning as a building block for machine learning network is a largely unexplored area. We present in this paper our results on the demonstration of an all optical associative learning element, realized on an integrated photonic platform using phase change materials combined with on-chip cascaded directional couplers. We implement the framework on our optical on-chip associative learning network, and experimentally demonstrate image classification on a publicly-accessible cat-dog dataset. The experimental implementation harnesses optical wavelength division-multiplexing, thus increasing the information channel capacity to process our machine learning task. Our unconventional approach to machine learning demonstrated experimentally on an optical platform could potentially open up new research possibilities in machine learning hardware architectures and algorithms.
Phase change materials are increasingly becoming important functional materials for applications in emerging integrated optics. Since the demonstration of a photonic phase change memory device in 2015, several new applications i this area have emerged ranging from lossless routing to on-chip photonics synapses. More recently the use of these materials in unconventional computing has seen an emerging interest, especially in the areas of optical abacuses and other forms of brain-inspired computing. There have also been advances in non-von Neumann approaches to carry out large-scale matrix multiplications. In this talk, I shall cover these topics and present a future view of these materials, not only in computation, but also in displays and holographic projections.
The use of photonics in computing is a hot topic of interest, driven by the need for ever-increasing speed along with reduced power consumption. In existing computing architectures, photonic data storage would dramatically improve the performance by reducing latencies associated with electrical memories. At the same time, the rise of ‘big data’ and ‘deep learning’ is driving the quest for non-von Neumann and brain-inspired computing paradigms. To succeed in both aspects, we have demonstrated non-volatile multi-level photonic memory avoiding the von Neumann bottleneck in the existing computing paradigm and a photonic synapse resembling the biological synapses for brain-inspired computing using phase-change materials (Ge2Sb2Te5).
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