Co-Design of Deep Neural Nets and Neural Net Accelerators for Embedded Vision Applications

2018/04/19 by Kiseok Kwon, Alon Amid, Kwon, Kiseok +10
Computer Science · Engineering · #Advanced Memory and Neural Computing #Advanced Neural Network Applications #CCD and CMOS Imaging Sensors #Distributed #FOS: Computer and information sciences #Neural Networks and Applications #Parallel #and Cluster Computing (cs.DC) #cs.DC

paper · pdf · doi:10.48550/arxiv.1804.10642

This paper is trimmed to 6 pages to meet the conference requirement. A longer version with more detailed discussion will be released afterwards

openalex publication_date 2018/04/19 · arxiv created 2018/04/20 · arxiv updated 2018/05/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Deep Learning is arguably the most rapidly evolving research area in recent years. As a result it is not surprising that the design of state-of-the-art deep neural net models proceeds without much consideration of the latest hardware targets, and the design of neural net accelerators proceeds without much consideration of the characteristics of the latest deep neural net models. Nevertheless, in this paper we show that there are significant improvements available if deep neural net models and neural net accelerators are co-designed.

Citations

• ImageNet classification with deep convolutional neural networks
• Keynote: Small Neural Nets Are Beautiful: Enabling Embedded Systems with Small Deep-Neural-Network Architectures

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