Using LegUp HLS to Synthesize a Deep CNN Inference Accelerator

By Jason Anderson,

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A paper describing the synthesis of a deep convolutional neural network (CNN) inference accelerator from C software with LegUp HLS will appear at the 2017 IEEE International System-on-Chip Conference (SOCC), at Munich, Germany, in September 2017. The work showcases the use of LegUp’s unique Pthreads flow to convert parallel software threads into spatial hardware parallelism. The accelerator incorporates a novel approach for zero-weight skipping, leveraging the ability to prune CNN weights with little inference accuracy loss.

J. H. Kim, B. Grady, R. Lian, J. Brothers, J.H. Anderson, “FPGA-Based CNN Inference Accelerator Synthesized from Multi-Threaded C Software,” IEEE SOCC, 2017 (PDF).

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