We propose RAPT, a reconfigurable accelerator for photonic training. RAPT is a low-power photonic accelerator that combines the benefits of phase change material (PCM) and photonics to implement both inference and training in one unified architecture, reconfigurable to support a variety of neural network models. Emerging silicon photonics has the potential to exploit parallelism of neural network models, reduce power consumption and provide high bandwidth density via wavelength division multiplexing making photonics an ideal candidate for on-device training and inference. As PCM is reconfigurable and non-volatile, we utilize PCM for the unique purposes of maintaining resonant wavelength without expensive electrical or thermal heaters and implementing non-linear activation function which eliminates the need to move data between memory and compute units; both of which leads to significant reduction in energy consumption and execution time.
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