A Winograd-based Integrated Photonics Accelerator for Convolutional Neural Networks
This work addresses the problem of high energy consumption in CNN hardware accelerators for applications like computer vision and speech recognition, representing an incremental advancement by integrating photonics with an existing algorithm.
The paper tackled the challenge of improving speed and energy efficiency in convolutional neural networks (CNNs) by proposing a photonics accelerator design based on the Winograd filtering algorithm, achieving results that compete with state-of-the-art electronic platforms and potentially improve energy efficiency by up to three orders of magnitude.
Neural Networks (NNs) have become the mainstream technology in the artificial intelligence (AI) renaissance over the past decade. Among different types of neural networks, convolutional neural networks (CNNs) have been widely adopted as they have achieved leading results in many fields such as computer vision and speech recognition. This success in part is due to the widespread availability of capable underlying hardware platforms. Applications have always been a driving factor for design of such hardware architectures. Hardware specialization can expose us to novel architectural solutions, which can outperform general purpose computers for tasks at hand. Although different applications demand for different performance measures, they all share speed and energy efficiency as high priorities. Meanwhile, photonics processing has seen a resurgence due to its inherited high speed and low power nature. Here, we investigate the potential of using photonics in CNNs by proposing a CNN accelerator design based on Winograd filtering algorithm. Our evaluation results show that while a photonic accelerator can compete with current-state-of-the-art electronic platforms in terms of both speed and power, it has the potential to improve the energy efficiency by up to three orders of magnitude.