High‐Throughput Multichannel Parallelized Diffraction Convolutional Neural Network Accelerator
Abstract
Convolutional neural networks are paramount in image and signal processing, and are responsible for the majority of image recognition power consumption today, concentrated mainly in convolution computations. With convolution operations being computationally intensive, next‐generation hardware accelerators need to offer parallelization and high efficiency. Diffractive optics offers the promise of low‐latency, highly parallel convolution operations. However, thus far parallelism is only partially harvested, thereby significantly underdelivering in comparison to its throughput potential. Here, a parallelized operation high‐throughput Fourier optic convolutional accelerator is demonstrated. For the first time, simultaneous processing of multiple kernels in Fourier domain enabled by optical diffraction orders is achieved alongside input parallelism. The proposed approach can offer ≈100× speedup over the previous generation optical diffraction‐based processor and 10× speedup over other state‐of‐the‐art optical Fourier classifiers.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 03, 2022
- Source ID
- 10.1002/lpor.202200213
Entities
People
- Maria Solyanik‐Gorgone
- Mario Miscuglio
- Puneet Gupta
- Russell L. T. Schwartz
- Shurui Li
- Volker Sorger
- Zibo Hu
Organizations
- George Washington University
- Office of Naval Research
- University of California