PCNNA: A Photonic Convolutional Neural Network Accelerator

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.Comment: 5 Pages, 6 Figures, IEEE SOCC 201

Temperature Dependence of a Sub-wavelength Compact Graphene Plasmon-Slot Modulator

We investigate a plasmonic electro-optic modulator with an extinction ratio exceeding 1 dB/um by engineering the optical mode to be in-plane with the graphene layer, and show how lowering the operating temperature enables steeper switching. We show how cooling Graphene enables steeping thus improving dynamic energy consumption. Further, we show that multi-layer Graphene integrated with a plasmonic slot waveguide allows for in-plane electric field components, and 3-dB device lengths as short as several hundred nanometers only. Compact modulators approaching electronic length-scales pave a way for ultra-dense photonic integrated circuits with smallest footprint