Efficient Compilation and Mapping of Fixed Function Combinational Logic onto Digital Signal Processors Targeting Neural Network Inference and Utilizing High-level Synthesis

• URL: http://arxiv.org/abs/2208.00302v1
• Date: Sat, 30 Jul 2022 20:11:59 GMT
• Title: Efficient Compilation and Mapping of Fixed Function Combinational Logic onto Digital Signal Processors Targeting Neural Network Inference and Utilizing High-level Synthesis
• Authors: Soheil Nazar Shahsavani, Arash Fayyazi, Mahdi Nazemi, and Massoud Pedram
• Abstract summary: Recent efforts for improving the performance of neural network (NN) accelerators have given rise to a new trend of logic-based NN inference relying on fixed function combinational logic. This paper presents an innovative design and optimization methodology for compilation and mapping of NNs, utilizing fixed function combinational logic to DSPs on FPGAs.
• Score: 3.83610794195621
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent efforts for improving the performance of neural network (NN) accelerators that meet today's application requirements have given rise to a new trend of logic-based NN inference relying on fixed function combinational logic. Mapping such large Boolean functions with many input variables and product terms to digital signal processors (DSPs) on Field-programmable gate arrays (FPGAs) needs a novel framework considering the structure and the reconfigurability of DSP blocks during this process. The proposed methodology in this paper maps the fixed function combinational logic blocks to a set of Boolean functions where Boolean operations corresponding to each function are mapped to DSP devices rather than look-up tables (LUTs) on the FPGAs to take advantage of the high performance, low latency, and parallelism of DSP blocks. % This paper also presents an innovative design and optimization methodology for compilation and mapping of NNs, utilizing fixed function combinational logic to DSPs on FPGAs employing high-level synthesis flow. % Our experimental evaluations across several \REVone{datasets} and selected NNs demonstrate the comparable performance of our framework in terms of the inference latency and output accuracy compared to prior art FPGA-based NN accelerators employing DSPs.

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