[2304.06745] End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs

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Our approach leverages Hessian-aware quantization (HAWQ) of NNs, the Quantized Open Neural Network Exchange (QONNX) intermediate representation, and the hls4ml tool flow for transpiling NNs into FPGA and ASIC firmware. This makes efficient NN implementations in hardware accessible to nonexperts, in a single open-sourced workflow that can be deployed for real-time machine learning applications in a wide range of scientific and industrial settings. We demonstrate the workflow in a particle physics application involving trigger decisions that must operate at the 40 MHz collision rate of the CERN Large Hadron Collider (LHC). Given the high collision rate, all data processing must be implemented on custom ASIC and FPGA hardware within a strict area and latency. Based on these constraints, we implement an optimized mixed-precision NN classifier for high-momentum particle jets in simulated LHC proton-proton collisions."/> <meta name="twitter:site" content="@arxiv"/> <meta name="twitter:card" content="summary"/> <meta name="twitter:title" content="End-to-end codesign of Hessian-aware quantized neural networks for..."/> <meta name="twitter:description" content="We develop an end-to-end workflow for the training and implementation of co-designed neural networks (NNs) for efficient field-programmable gate array (FPGA) and application-specific integrated..."/> <meta name="twitter:image" content="https://static.arxiv.org/icons/twitter/arxiv-logo-twitter-square.png"/> <meta name="twitter:image:alt" content="arXiv logo"/> <link rel="stylesheet" media="screen" type="text/css" href="/static/browse/0.3.4/css/tooltip.css"/><link rel="stylesheet" media="screen" type="text/css" href="https://static.arxiv.org/js/bibex-dev/bibex.css?20200709"/> <script src="/static/browse/0.3.4/js/mathjaxToggle.min.js" type="text/javascript"></script> <script src="//code.jquery.com/jquery-latest.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/js-cookie@2/src/js.cookie.min.js" type="text/javascript"></script> <script src="//cdn.jsdelivr.net/npm/dompurify@2.3.5/dist/purify.min.js"></script> <script src="/static/browse/0.3.4/js/toggle-labs.js?20241022" type="text/javascript"></script> <script src="/static/browse/0.3.4/js/cite.js" type="text/javascript"></script><meta name="citation_title" content="End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs" /><meta name="citation_author" content="Campos, Javier" /><meta name="citation_author" content="Dong, Zhen" /><meta name="citation_author" content="Duarte, Javier" /><meta name="citation_author" content="Gholami, Amir" /><meta name="citation_author" content="Mahoney, Michael W." /><meta name="citation_author" content="Mitrevski, Jovan" /><meta name="citation_author" content="Tran, Nhan" /><meta name="citation_date" content="2023/04/13" /><meta name="citation_online_date" content="2023/04/13" /><meta name="citation_pdf_url" content="http://arxiv.org/pdf/2304.06745" /><meta name="citation_arxiv_id" content="2304.06745" /><meta name="citation_abstract" content="We develop an end-to-end workflow for the training and implementation of co-designed neural networks (NNs) for efficient field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) hardware. Our approach leverages Hessian-aware quantization (HAWQ) of NNs, the Quantized Open Neural Network Exchange (QONNX) intermediate representation, and the hls4ml tool flow for transpiling NNs into FPGA and ASIC firmware. This makes efficient NN implementations in hardware accessible to nonexperts, in a single open-sourced workflow that can be deployed for real-time machine learning applications in a wide range of scientific and industrial settings. We demonstrate the workflow in a particle physics application involving trigger decisions that must operate at the 40 MHz collision rate of the CERN Large Hadron Collider (LHC). Given the high collision rate, all data processing must be implemented on custom ASIC and FPGA hardware within a strict area and latency. Based on these constraints, we implement an optimized mixed-precision NN classifier for high-momentum particle jets in simulated LHC proton-proton collisions." /> </head> <body class="with-cu-identity"> <div class="flex-wrap-footer"> <header> <a href="#content" class="is-sr-only">Skip to main content</a>  <div class="columns is-vcentered is-hidden-mobile" id="cu-identity"> <div class="column" id="cu-logo"> <a href="https://www.cornell.edu/"><img src="/static/browse/0.3.4/images/icons/cu/cornell-reduced-white-SMALL.svg" alt="Cornell University" /></a> </div><div class="column" id="support-ack"> <span id="support-ack-url">We gratefully acknowledge support from the Simons Foundation, <a href="https://info.arxiv.org/about/ourmembers.html">member institutions</a>, and all contributors.</span> <a href="https://info.arxiv.org/about/donate.html" class="btn-header-donate">Donate</a> </div> </div> <div id="header" class="is-hidden-mobile"> <a aria-hidden="true" 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href="https://arxiv.org/search/cs?searchtype=author&query=Campos,+J" rel="nofollow">Javier Campos</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Dong,+Z" rel="nofollow">Zhen Dong</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Duarte,+J" rel="nofollow">Javier Duarte</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Gholami,+A" rel="nofollow">Amir Gholami</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mahoney,+M+W" rel="nofollow">Michael W. Mahoney</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Mitrevski,+J" rel="nofollow">Jovan Mitrevski</a>, <a href="https://arxiv.org/search/cs?searchtype=author&query=Tran,+N" rel="nofollow">Nhan Tran</a></div> <div id="download-button-info" hidden>View a PDF of the paper titled End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs, by Javier Campos and 6 other authors</div> <a class="mobile-submission-download" href="/pdf/2304.06745">View PDF</a> <blockquote class="abstract mathjax"> <span class="descriptor">Abstract:</span>We develop an end-to-end workflow for the training and implementation of co-designed neural networks (NNs) for efficient field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) hardware. Our approach leverages Hessian-aware quantization (HAWQ) of NNs, the Quantized Open Neural Network Exchange (QONNX) intermediate representation, and the hls4ml tool flow for transpiling NNs into FPGA and ASIC firmware. This makes efficient NN implementations in hardware accessible to nonexperts, in a single open-sourced workflow that can be deployed for real-time machine learning applications in a wide range of scientific and industrial settings. We demonstrate the workflow in a particle physics application involving trigger decisions that must operate at the 40 MHz collision rate of the CERN Large Hadron Collider (LHC). Given the high collision rate, all data processing must be implemented on custom ASIC and FPGA hardware within a strict area and latency. Based on these constraints, we implement an optimized mixed-precision NN classifier for high-momentum particle jets in simulated LHC proton-proton collisions. </blockquote>  <div class="metatable"> <table summary="Additional metadata"> <tr> <td class="tablecell label">Comments:</td> <td class="tablecell comments mathjax">19 pages, 6 figures, 2 tables</td> </tr> <tr> <td class="tablecell label">Subjects:</td> <td class="tablecell subjects"> <span class="primary-subject">Machine Learning (cs.LG)</span>; Hardware Architecture (cs.AR); High Energy Physics - Experiment (hep-ex); Instrumentation and Detectors (physics.ins-det)</td> </tr> <tr> <td class="tablecell label">Report number:</td> <td class="tablecell jref">FERMILAB-PUB-23-150-CSAID-ETD</td> </tr> <tr> <td class="tablecell label">Cite as:</td> <td class="tablecell arxivid"><span class="arxivid"><a href="https://arxiv.org/abs/2304.06745">arXiv:2304.06745</a> [cs.LG]</span></td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxividv">(or <span class="arxivid"> <a href="https://arxiv.org/abs/2304.06745v1">arXiv:2304.06745v1</a> [cs.LG]</span> for this version) </td> </tr> <tr> <td class="tablecell label"> </td> <td class="tablecell arxivdoi"> <a href="https://doi.org/10.48550/arXiv.2304.06745" id="arxiv-doi-link">https://doi.org/10.48550/arXiv.2304.06745</a><div class="button-and-tooltip"> <button class="more-info" aria-describedby="more-info-desc-1"> <svg height="15" role="presentation" xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512"><path fill="currentColor" d="M256 8C119.043 8 8 119.083 8 256c0 136.997 111.043 248 248 248s248-111.003 248-248C504 119.083 392.957 8 256 8zm0 110c23.196 0 42 18.804 42 42s-18.804 42-42 42-42-18.804-42-42 18.804-42 42-42zm56 254c0 6.627-5.373 12-12 12h-88c-6.627 0-12-5.373-12-12v-24c0-6.627 5.373-12 12-12h12v-64h-12c-6.627 0-12-5.373-12-12v-24c0-6.627 5.373-12 12-12h64c6.627 0 12 5.373 12 12v100h12c6.627 0 12 5.373 12 12v24z" class=""></path></svg> 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[2304.06745] End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs