Batch Processing and Data Streaming Fourier-based Convolutional Neural Network Accelerator

• URL: http://arxiv.org/abs/2112.12297v1
• Date: Thu, 23 Dec 2021 01:06:17 GMT
• Title: Batch Processing and Data Streaming Fourier-based Convolutional Neural Network Accelerator
• Authors: Zibo Hu, Shurui Li, Russell L.T. Schwartz, Maria Solyanik-Gorgone, Mario Miscuglio, Puneet Gupta, Volker J. Sorger
• Abstract summary: Decision-making by artificial neural networks with minimal latency is paramount for numerous applications such as navigation, tracking, and real-time machine action systems. This requires the machine learning hardware to handle multidimensional data with a high throughput. We demonstrate a non-van Neuman-based machine learning acceleration with a Fourier Convolutional Neural Network (FCNN) accelerator.
• Score: 4.7257913147626995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Decision-making by artificial neural networks with minimal latency is paramount for numerous applications such as navigation, tracking, and real-time machine action systems. This requires the machine learning hardware to handle multidimensional data with a high throughput. Processing convolution operations being the major computational tool for data classification tasks, unfortunately, follows a challenging run-time complexity scaling law. However, implementing the convolution theorem homomorphically in a Fourier-optic display-light-processor enables a non-iterative O(1) runtime complexity for data inputs beyond 1,000 x 1,000 large matrices. Following this approach, here we demonstrate data streaming multi-kernel image batch-processing with a Fourier Convolutional Neural Network (FCNN) accelerator. We show image batch processing of large-scale matrices as passive 2-million dot-product multiplications performed by digital light-processing modules in the Fourier domain. In addition, we parallelize this optical FCNN system further by utilizing multiple spatio-parallel diffraction orders, thus achieving a 98-times throughput improvement over state-of-art FCNN accelerators. The comprehensive discussion of the practical challenges related to working on the edge of the system's capabilities highlights issues of crosstalk in the Fourier domain and resolution scaling laws. Accelerating convolutions by utilizing the massive parallelism in display technology brings forth a non-van Neuman-based machine learning acceleration.

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