  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.
