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SCDVP: A Simplicial CNN Digital Visual Processor
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DOI：10.1109/TCSI.2013.2295959.png)
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Abstract
En 中文
In this work we present a programmable and reconfigurable single instruction multiple data (SIMD) visual processor based on the S-CNN architecture, namely, the Simplicial CNN Digital Visual Processor (SCDVP), oriented to high-performance low-level image processing. The cells in the array have a selectable neighborhood configuration and several registers, which provide the chip with extended spatial and temporal processing capabilities, in particular optical flow. A prototype 64 X 64 cell chip with two program memories and a column adder was fabricated in a 90 nm technology, which running at 133 MHz delivers 105.5 GOPS. The calculation at the cell level is performed with time coded signals and the program memory is located outside the array. This produces a very efficient realization in terms of area: 53.8 GOPS per mm, which outperforms all results reported so far. We show that even after normalization, to account for technology scaling, the proposed architecture is the most efficient among all reported digital processors. Computation performance to power ratio also exceeds all previous results with 817.8 GOPS/W. Experimental results of the working chip are reported.
Keywords:
Cellular neural networks (CNN)
simplicial computation
image processing
pixel level processing
vision chip
ASIC
piecewise linear
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