Higher-dimensional processing using a photonic tensor core with continuous-time data

New developments in hardware-based 'accelerators' range from electronic tensor cores and memristor-based arrays to photonic implementations. The goal of these approaches is to handle the exponentially growing computational load of machine learning, which currently requires the doubling of hardware capability approximately every 3.5 months. One solution is increasing the data dimensionality that is processable by such hardware. Although two-dimensional data processing by multiplexing space and wavelength has been previously reported, the use of three-dimensional processing has not yet been implemented in hardware. In this paper, we introduce the radio-frequency modulation of photonic signals to increase parallelization, adding an additional dimension to the data alongside spatially distributed non-volatile memories and wavelength multiplexing. We leverage higher-dimensional processing to configure such a system to an architecture compatible with edge computing frameworks. Our system achieves a parallelism of 100, two orders higher than implementations using only the spatial and wavelength degrees of freedom. We demonstrate this by performing a synchronous convolution of 100 clinical electrocardiogram signals from patients with cardiovascular diseases, and constructing a convolutional neural network capable of identifying patients at sudden death risk with 93.5% accuracy.

Automated summary

This paper introduces new developments in hardware-based accelerators, including electronic tensor cores and photonic implementations. By modulating photonic signals and combining them with distributed memories and wavelength multiplexing, we configure the system to be compatible with edge computing frameworks. Through processing electrocardiogram data and constructing a convolutional neural network, we demonstrate that this method can accurately identify patients at risk of sudden cardiac death.