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

Primer on silicon neuromorphic photonic processors: architecture and compiler

Microelectronic computers have encountered challenges in meeting all of today’s demands for information processing. Meeting these demands will require the development of unconventional computers employing alternative processing models and new device physics. Neural network models have come to dominate modern machine learning algorithms, and specialized electronic hardware has been developed to implement them more efficiently. A silicon photonic integration industry promises to bring manufacturing ecosystems normally reserved for microelectronics to photonics. Photonic devices have already found simple analog signal processing niches where electronics cannot provide sufficient bandwidth and reconfigurability. In order to solve more complex information processing problems, they will have to adopt a processing model that generalizes and scales. Neuromorphic photonics aims to map physical models of optoelectronic systems to abstract models of neural networks. It represents a new opportunity for machine information processing on sub-nanosecond timescales, with application to mathematical programming, intelligent radio frequency signal processing, and real-time control. The strategy of neuromorphic engineering is to externalize the risk of developing computational theory alongside hardware. The strategy of remaining compatible with silicon photonics externalizes the risk of platform development. In this perspective article, we provide a rationale for a neuromorphic photonics processor, envisioning its architecture and a compiler. We also discuss how it can be interfaced with a general purpose computer, i.e. a CPU, as a coprocessor to target specific applications. This paper is intended for a wide audience and provides a roadmap for expanding research in the direction of transforming neuromorphic photonics into a viable and useful candidate for accelerating neuromorphic computing

Defect Restoration of Low-Temperature Sol-Gel-Derived ZnO via Sulfur Doping for Advancing Polymeric Schottky Photodiodes

This study shows that the deep-level defect states in sol-gel-derived ZnO can be efficiently restored by facile sulfur doping chemistry, wherein the +2 charged oxygen vacancies are filled with the S2- ions brought by thiocyanate. By fabricating a solution-processed polymeric Schottky diode with ITO/ZnO as the cathode, the synergetic effects of such defect-restored ZnO electron selective layers are demonstrated. The decreased chemical defects and thus reduced mid-gap states enable to not only enlarge the effective built-in potential, which can expand the width of the depletion region, but also increase the Schottky energy barrier, which can reduce undesired dark-current injection. As a result, the demonstrated simple-structure blue-selective polymeric Schottky photodiode renders near-ideal diode operation with an ideality factor of 1.18, a noise equivalent power of 1.25 × 10-14 W Hz-1/2, and a high peak detectivity of 2.4 × 1013 Jones. In addition, the chemical robustness of sulfur-doped ZnO enables exceptional device stability against air exposure as well as device-to-device reproducibility. Therefore, this work opens the possibility of utilizing low-temperature sol-gel-derived ZnO in realizing high-performance, stable, and reliable organic photodiodes that could be employed in the design of practical image sensors. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.1