Dither NN: Hardware/Algorithm Co-Design for Accurate Quantized Neural Networks

Kota ANDO  Kodai UEYOSHI  Yuka OBA  Kazutoshi HIROSE  Ryota UEMATSU  Takumi KUDO  Masayuki IKEBE  Tetsuya ASAI  Shinya TAKAMAEDA-YAMAZAKI  Masato MOTOMURA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.12   pp.2341-2353
Publication Date: 2019/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2019PAP0009
Type of Manuscript: Special Section PAPER (Special Section on Parallel and Distributed Computing and Networking)
Category: Computer System
Keyword: 
neural network,  dithering,  error diffusion,  FPGA,  hardware-oriented neural network algorithm,  

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Summary: 
Deep neural network (NN) has been widely accepted for enabling various AI applications, however, the limitation of computational and memory resources is a major problem on mobile devices. Quantized NN with a reduced bit precision is an effective solution, which relaxes the resource requirements, but the accuracy degradation due to its numerical approximation is another problem. We propose a novel quantized NN model employing the “dithering” technique to improve the accuracy with the minimal additional hardware requirement at the view point of the hardware-algorithm co-designing. Dithering distributes the quantization error occurring at each pixel (neuron) spatially so that the total information loss of the plane would be minimized. The experiment we conducted using the software-based accuracy evaluation and FPGA-based hardware resource estimation proved the effectiveness and efficiency of the concept of an NN model with dithering.