计算机科学
瓶颈
卷积神经网络
卷积(计算机科学)
核(代数)
巨量平行
吞吐量
可扩展性
延迟(音频)
低延迟(资本市场)
调制(音乐)
计算机工程
人工神经网络
并行计算
人工智能
嵌入式系统
计算机网络
电信
无线
物理
数据库
声学
组合数学
数学
作者
Puneet Gupta,Shurui Li
摘要
Low latency, high throughput inference on Convolution Neural Networks (CNNs) remains a challenge, especially for applications requiring large input or large kernel sizes. 4F optics provides a solution to potentially accelerate CNN inferences with Fourier optics and the well-known convolution theorem. However, existing 4F CNN accelerators suffer from various limitations that make the implementation of a multi-channel, multi-layer CNN not scalable or even impractical. In this paper, we discuss the limitations of 4F CNN accelerators including the positive sensor readout, intensity-only modulation and slow modulation frequency and methods to address them. We also propose the channel tiling method that can address an important throughput and precision bottleneck of high-speed, massively-parallel optical 4F computing systems, not requiring any additional optical hardware.
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