Let Coarse-Grained Resources Be Shared: Mapping Entire Neural Networks on FPGAs
Article No.: 114, Pages 1 - 23
Abstract
Traditional High-Level Synthesis (HLS) provides rapid prototyping of hardware accelerators without coding with Hardware Description Languages (HDLs). However, such an approach does not well support allocating large applications like entire deep neural networks on a single Field Programmable Gate Array (FPGA) device. The approach leads to designs that are inefficient or do not fit into FPGAs due to resource constraints.
This work proposes to shrink generated designs by coarse-grained resource control based on function sharing in functional Intermediate Representations (IRs). The proposed compiler passes and rewrite system aim at producing valid design points and removing redundant hardware. Such optimizations make fitting entire neural networks on FPGAs feasible and produce competitive performance compared to running specialized kernels for each layer.
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- Let Coarse-Grained Resources Be Shared: Mapping Entire Neural Networks on FPGAs
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October 2023
1394 pages
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Publication History
Published: 09 September 2023
Accepted: 13 July 2023
Revised: 02 June 2023
Received: 23 March 2023
Published in TECS Volume 22, Issue 5s
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