Brain derived vision algorithm on high performance architectures

Authors:
Jayram Moorkanikara Nageswaran;Andrew Felch;Ashok Chandrasekhar;Nikil Dutt;Richard Granger;Alex Nicolau;Alex Veidenbaum
Affiliations:
Department of Computer Science, University of California, Irvine;Neukom Institute for Computational Science, Dartmouth College, Hanover, NH;Neukom Institute for Computational Science, Dartmouth College, Hanover, NH;Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA;Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH;Department of Computer Science, Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA;Department of Computer Science, Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA
Venue:
International Journal of Parallel Programming
Year:
2009

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Abstract

Even though computing systems have increased the number of transistors, the switching speed, and the number of processors, most programs exhibit limited speedup due to the serial dependencies of existing algorithms. Analysis of intrinsically parallel systems such as brain circuitry have led to the identification of novel architecture designs, and also new algorithms than can exploit the features of modern multiprocessor systems. In this article we describe the details of a brain derived vision (BDV) algorithm that is derived from the anatomical structure, and physiological operating principles of thalamo-cortical brain circuits. We show that many characteristics of the BDV algorithm lend themselves to implementation on IBM CELL architecture, and yield impressive speedups that equal or exceed the performance of specialized solutions such as FPGAs. Mapping this algorithm to the IBM CELL is non-trivial, and we suggest various approaches to deal with parallelism, task granularity, communication, and memory locality. We also show that a cluster of three PS3s (or more) containing IBM CELL processors provides a promising platform for brain derived algorithms, exhibiting speedup of more than 140× over a desktop PC implementation, and thus enabling real-time object recognition for robotic systems.