GPU-Accelerated High-Level Synthesis for Bitwidth Optimization of FPGA Datapaths

Nachiket Kapre, Deheng Ye
School of Computer Engineering, Nanyang Technological University, Singapore 639798
International Symposium on Field-Programmable Gate Arrays, 2016


   title={GPU-Accelerated High-Level Synthesis for Bitwidth Optimization of FPGA Datapaths},

   author={Kapre, Nachiket and Ye, Deheng},



Bitwidth optimization of FPGA datapaths can save hardware resources by choosing the fewest number of bits required for each datapath variable to achieve a desired quality of result. However, it is an NP-hard problem that requires unacceptably long runtimes when using sequential CPU-based heuristics. We show how to parallelize the key steps of bitwidth optimization on the GPU by performing a fast brute-force search over a carefully constrained search space. We develop a high-level synthesis methodology suitable for rapid prototyping of bitwidth-annotated RTL code generation using gcc’s GIMPLE backend. For range analysis, we perform parallel evaluation of sub-intervals to provide tighter bounds compared to ordinary interval arithmetic. For bitwidth allocation, we enumerate the different bitwidth combinations in parallel by assigning each combination to a GPU thread. We demonstrate up to 10-1000x speedups for range analysis and 50-200x speedups for bitwidth allocation when comparing NVIDIA K20 GPU implementation to an Intel Core i5-4570 CPU while maintaining identical solution quality across various benchmarks. This allows us to generate tailor-made RTL with minimum bitwidths in hundreds of milliseconds instead of hundreds of minutes when starting from high-level C descriptions of dataflow computations.
VN:F [1.9.22_1171]
Rating: 1.0/5 (1 vote cast)
GPU-Accelerated High-Level Synthesis for Bitwidth Optimization of FPGA Datapaths, 1.0 out of 5 based on 1 rating

* * *

* * *

TwitterAPIExchange Object
    [oauth_access_token:TwitterAPIExchange:private] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
    [oauth_access_token_secret:TwitterAPIExchange:private] => o29ji3VLVmB6jASMqY8G7QZDCrdFmoTvCDNNUlb7s
    [consumer_key:TwitterAPIExchange:private] => TdQb63pho0ak9VevwMWpEgXAE
    [consumer_secret:TwitterAPIExchange:private] => Uq4rWz7nUnH1y6ab6uQ9xMk0KLcDrmckneEMdlq6G5E0jlQCFx
    [postfields:TwitterAPIExchange:private] => 
    [getfield:TwitterAPIExchange:private] => ?cursor=-1&screen_name=hgpu&skip_status=true&include_user_entities=false
    [oauth:protected] => Array
            [oauth_consumer_key] => TdQb63pho0ak9VevwMWpEgXAE
            [oauth_nonce] => 1485049500
            [oauth_signature_method] => HMAC-SHA1
            [oauth_token] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
            [oauth_timestamp] => 1485049500
            [oauth_version] => 1.0
            [cursor] => -1
            [screen_name] => hgpu
            [skip_status] => true
            [include_user_entities] => false
            [oauth_signature] => Y+FQYaVdlT2p1wtSssP/8FAW9u0=

    [url] => https://api.twitter.com/1.1/users/show.json
Follow us on Facebook
Follow us on Twitter

HGPU group

2137 peoples are following HGPU @twitter

HGPU group © 2010-2017 hgpu.org

All rights belong to the respective authors

Contact us: