NDLI: Stochastic-Based Multi-stage Streaming Realization of a Deep Convolutional Neural Network (Abstract Only)

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Stochastic-Based Multi-stage Streaming Realization of a Deep Convolutional Neural Network (Abstract Only)

Content Provider	ACM Digital Library
Author	Lin, Mingjie Alawad, Mohammed
Abstract	Large-scale convolutional neural network (CNN), conceptually mimicking the operational principle of visual perception in human brain, has been widely applied to tackle many challenging computer vision and artificial intelligence applications. Unfortunately, despite of its simple architecture, a typically sized CNN is well known to be computationally intensive. This work presents a novel stochastic-based and scalable hardware architecture and circuit design that computes a large-scale CNN with FPGA. The key idea is to implement all key components of a deep learning CNN, including multi-dimensional convolution, activation, and pooling layers, completely in the probabilistic computing domain in order to achieve high computing robustness, high performance, and low hardware usage. Most importantly, through both theoretical analysis and FPGA hardware implementation, we demonstrate that stochastic-based deep CNN can achieve superior hardware scalability when compared with its conventional deterministic-based FPGA implementation by allowing a stream computing mode and adopting efficient random sample manipulations. Overall, being highly scalable and energy efficient, our stochastic-based convolutional neural network architecture is well-suited for a modular vision engine with the goal of performing real-time detection, recognition and segmentation of mega-pixel images, especially those perception-based computing tasks that are inherently fault-tolerant, while still requiring high energy efficiency.
Starting Page	291
Ending Page	291
Page Count	1
ISBN	9781450343541
DOI	10.1145/3020078.3021788
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-02-22
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Fpga Convolutional neural network Stochastic computing
Content Type	Text
Resource Type	Article

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