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Volume: 29 | Article ID: art00009
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Efficient Pre-Processor for CNN
  DOI :  10.2352/ISSN.2470-1173.2017.19.AVM-020  Published OnlineJanuary 2017
Abstract

Convolution Neural Networks (CNN) are rapidly deployed in ADAS and Autonomous driving for object detection, recognition, and semantic segmentation. The prior art of supporting CNN (HW IP or multi-core SW) doesn't address efficient implementation for the first layer, YUV color space, and output stride support. The given paper proposes a new pre-processing technique to enhance CNN based HW IP or multi-core SW solution. The pre-processor enables new features namely (1) Higher parallelism for the first layer with boosting of first layer (2) Efficient YUV color space (3) Efficient output stride support. The pre-processor uses novel phase-split method to enable supporting above features. The proposed solution splits input to multiple phases based on spatial location e.g. 2 phases for YUV 4:2:0 format, 4 phases for output strides 2 etc. The proposed solution is a unified solution that enables utilization (>90%) for the first layer and reduction of bandwidth of 2-4x for output stride of 2. For YUV color space, this reduces the computation by factor 2 along saving of ∼0.1 mm2 of silicon area with negligible loss in accuracy.

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Mihir Mody, Manu Mathew, Shyam Jagannathan, "Efficient Pre-Processor for CNNin Proc. IS&T Int’l. Symp. on Electronic Imaging: Autonomous Vehicles and Machines,  2017,  pp 50 - 53,  https://doi.org/10.2352/ISSN.2470-1173.2017.19.AVM-020

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Copyright © Society for Imaging Science and Technology 2017
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Electronic Imaging
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