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In this paper we present a scalable dataflow hardware architecture optimized for the computation of general-purpose vision algorithms - neuFlow - and a dataflow compiler - luaFlow - that transforms high-level flow-graph representations of these algorithms into machine code for neuFlow. This system was designed with the goal of providing real-time detection, categorization and localization of objects in complex scenes, while consuming 10 Watts when implemented on a Xilinx Virtex 6 FPGA platform, or about ten times less than a laptop computer, and producing speedups of up to 100 times in real-world applications. We present an application of the system on street scene analysis, segmenting 20 categories on 500 × 375 frames at 12 frames per second on our custom hardware neuFlow.
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Clément Farabet
Berin Martini
Benoit Corda
Yale University
New York University
Courant Institute of Mathematical Sciences
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Farabet et al. (Wed,) studied this question.
www.synapsesocial.com/papers/6a08ff440465d979db9d0e11 — DOI: https://doi.org/10.1109/cvprw.2011.5981829