Abstract
Deep learning has become the most mainstream technology in artificial intelligence (AI) because it can be comparable to human performance in complex tasks. However, in the era of big data, the ever-increasing data volume and model scale makes deep learning require mighty computing power and acceptable energy costs. For electrical chips, including most deep learning accelerators, transistor performance limitations make it challenging to meet computing’s energy efficiency requirements. Silicon photonic devices are expected to replace transistors and become the mainstream components in computing architecture due to their advantages, such as low energy consumption, large bandwidth, and high speed. Therefore, we propose a silicon photonic-assisted deep learning accelerator for big data. The accelerator uses microring resonators (MRs) to form a photonic multiplication array. It combines photonic-specific wavelength division multiplexing (WDM) technology to achieve multiple parallel calculations of input feature maps and convolution kernels at the speed of light, providing the promise of energy efficiency and calculation speed improvement. The proposed accelerator achieves at least a 75x improvement in computational efficiency compared to the traditional electrical design.
Highlights
In a modern society driven by big data, artificial intelligence (AI) has brought great convenience to human life
As an indispensable part of solving complex problems in the field of AI, deep learning has been used in many applications, e.g., image and speech recognition, machine translation, self-driving, Internet of Things (IoTs), 5th generation (5G) mobile networks, and edge computing [1,2,3,4,5,6,7,8,9,10,11,12,13]
We introduce an analytical model to identify the number of microring resonators (MRs) used, power consumption, area, and execution time in each layer of the convolutional neural networks (CNNs)
Summary
In a modern society driven by big data, artificial intelligence (AI) has brought great convenience to human life. Benefitting from the peaceful development of photonic integration technology and manufacturing platform, various mature active and passive building blocks have been demonstrated experimentally, such as modulators, photodetectors, splitters, wavelength multiplexers, and filters [28,29,30,31] Based on these photonic devices, photonic computing elements such as photonic adders, differentiators, integrators, and multipliers can be realized [32,33,34,35]. We first use the mature microring resonators (MRs) as the basic unit to design a photonic matrix-vector multiplier (PMVM) to perform the most complex convolution operation on CNNs. we introduce an analytical model to identify the number of MRs used, power consumption, area, and execution time in each layer of the CNNs. At last, we introduce our PMVM-based photonicassisted CNN accelerator architecture and its workflow.
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