  Convolutional Neural Networks (CNN) have been the centerpiece of many
applications including but not limited to computer vision, speech processing,
and Natural Language Processing (NLP). However, the computationally expensive
convolution operations impose many challenges to the performance and
scalability of CNNs. In parallel, photonic systems, which are traditionally
employed for data communication, have enjoyed recent popularity for data
processing due to their high bandwidth, low power consumption, and
reconfigurability. Here we propose a Photonic Convolutional Neural Network
Accelerator (PCNNA) as a proof of concept design to speedup the convolution
operation for CNNs. Our design is based on the recently introduced silicon
photonic microring weight banks, which use broadcast-and-weight protocol to
perform Multiply And Accumulate (MAC) operation and move data through layers of
a neural network. Here, we aim to exploit the synergy between the inherent
parallelism of photonics in the form of Wavelength Division Multiplexing (WDM)
and sparsity of connections between input feature maps and kernels in CNNs.
While our full system design offers up to more than 3 orders of magnitude
speedup in execution time, its optical core potentially offers more than 5
order of magnitude speedup compared to state-of-the-art electronic
counterparts.
