Digital Electronics and Analog Photonics for Convolutional Neural Networks (DEAP-CNNs)
This addresses the problem of high computational costs in CNNs for applications like computer vision and natural language processing, offering a potential speedup for hardware acceleration.
The paper tackles the computational expense of convolutions in digital electronics by proposing a Digital Electronic and Analog Photonic (DEAP) CNN hardware architecture, which has the potential to be 2.8 to 14 times faster while maintaining the same power usage as current state-of-the-art GPUs.
Convolutional Neural Networks (CNNs) are powerful and highly ubiquitous tools for extracting features from large datasets for applications such as computer vision and natural language processing. However, a convolution is a computationally expensive operation in digital electronics. In contrast, neuromorphic photonic systems, which have experienced a recent surge of interest over the last few years, propose higher bandwidth and energy efficiencies for neural network training and inference. Neuromorphic photonics exploits the advantages of optical electronics, including the ease of analog processing, and busing multiple signals on a single waveguide at the speed of light. Here, we propose a Digital Electronic and Analog Photonic (DEAP) CNN hardware architecture that has potential to be 2.8 to 14 times faster while maintaining the same power usage of current state-of-the-art GPUs.