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.
No takes yet. Share an insight, caveat, or question.
Farabet et al. (2011) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: