Photonic-Enabled Energy-Efficient Transparent Neuromorphic Computing Devices: A Review
Abstract
In the evolving field of artificial intelligence (AI), two notable trends are emerging: the rapid growth of large AI model sizes and the surge of vast amounts of data. Moore's law emphasizes the need for alternative computing paradigms to meet the rising demand for computational power and address von Neumann model constraints. Nowadays, neuromorphic computing, inspired by the mechanisms and functionality of human brains, uses physical artificial neurons to do computations and is drawing widespread attention. Neuromorphic computing aims to emulate brain-like information processing with co-localized memory and logic, breaking the von Neumann bottleneck. In this regard, the integration of photonic materials in computing led to growth in photonic computing, where light is a fundamental source of energy and can also be utilized as a signal for neuromorphic computing. Photonic computing enables ultrafast artificial neural networks with sub-nanosecond latencies and low heat dissipation. However, current neuromorphic technologies still struggle to achieve petascale speed and energy efficiency. Additionally, the general benefits of neuromorphic computing and AI can be realized in an optically transparent manner, broadening their applications in bionics and human interfaces. This review explores the suitability and design strategies for transparent photonic devices that create artificial interfaces mimicking natural functions.
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