Paper
28 May 2009 Tiled architecture of a CNN-mostly IP system
Lambert Spaanenburg, Suleyman Malki
Author Affiliations +
Proceedings Volume 7363, VLSI Circuits and Systems IV; 736313 (2009) https://doi.org/10.1117/12.821904
Event: SPIE Europe Microtechnologies for the New Millennium, 2009, Dresden, Germany
Abstract
Multi-core architectures have been popularized with the advent of the IBM CELL. On a finer grain the problems in scheduling multi-cores have already existed in the tiled architectures, such as the EPIC and Da Vinci. It is not easy to evaluate the performance of a schedule on such architecture as historical data are not available. One solution is to compile algorithms for which an optimal schedule is known by analysis. A typical example is an algorithm that is already defined in terms of many collaborating simple nodes, such as a Cellular Neural Network (CNN). A simple node with a local register stack together with a 'rotating wheel' internal communication mechanism has been proposed. Though the basic CNN allows for a tiled implementation of a tiled algorithm on a tiled structure, a practical CNN system will have to disturb this regularity by the additional need for arithmetical and logical operations. Arithmetic operations are needed for instance to accommodate for low-level image processing, while logical operations are needed to fork and merge different data streams without use of the external memory. It is found that the 'rotating wheel' internal communication mechanism still handles such mechanisms without the need for global control. Overall the CNN system provides for a practical network size as implemented on a FPGA, can be easily used as embedded IP and provides a clear benchmark for a multi-core compiler.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lambert Spaanenburg and Suleyman Malki "Tiled architecture of a CNN-mostly IP system", Proc. SPIE 7363, VLSI Circuits and Systems IV, 736313 (28 May 2009); https://doi.org/10.1117/12.821904
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KEYWORDS
Veins

Image segmentation

Biometrics

Image processing

Neural networks

Field programmable gate arrays

Network architectures

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