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FPGA-based stochastic gradient descent (powered by ZipML - Low-precision machine learning on reconfigurable hardware)

License: GNU Affero General Public License v3.0

Shell 4.89% C++ 10.54% Python 0.48% C 1.44% Tcl 6.87% VHDL 63.90% Verilog 3.41% Coq 0.68% SystemVerilog 4.66% Makefile 0.14% HTML 3.00%

zipml-xeonfpga's Introduction

ZipML on Xeon+FPGA

See wiki

Citation

If you find ZipML-PYNQ useful, please consider citing ZipML papers:

@inproceedings{kara2017fpga, title={FPGA-accelerated Dense Linear Machine Learning: A Precision-Convergence Trade-off}, author={Kara, Kaan and Alistarh, Dan and Alonso, Gustavo and Mutlu, Onur and Zhang, Ce}, booktitle={Field-Programmable Custom Computing Machines (FCCM), 2017 IEEE 25th Annual International Symposium on}, pages={160--167}, year={2017}, organization={IEEE} }

zipml-xeonfpga's People

Contributors

kaankara avatar

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