{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T02:04:28Z","timestamp":1767924268347,"version":"3.49.0"},"reference-count":55,"publisher":"Association for Computing Machinery (ACM)","issue":"5s","license":[{"start":{"date-parts":[[2023,9,9]],"date-time":"2023-09-09T00:00:00Z","timestamp":1694217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"CominLabs Excellence Laboratory"},{"name":"National Research Agency","award":["ANR-10-LABX-07-01"],"award-info":[{"award-number":["ANR-10-LABX-07-01"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Embed. Comput. Syst."],"published-print":{"date-parts":[[2023,10,31]]},"abstract":"<jats:p>Neural networks are increasingly being used as components in safety-critical applications, for instance, as controllers in embedded systems. Their formal safety verification has made significant progress but typically considers only idealized real-valued networks. For practical applications, such neural networks have to be quantized, i.e., implemented in finite-precision arithmetic, which inevitably introduces roundoff errors. Choosing a suitable precision that is both guaranteed to satisfy a roundoff error bound to ensure safety and that is as small as possible to not waste resources is highly nontrivial to do manually. This task is especially challenging when quantizing a neural network in fixed-point arithmetic, where one can choose among a large number of precisions and has to ensure overflow-freedom explicitly.<\/jats:p>\n          <jats:p>This paper presents the first sound and fully automated mixed-precision quantization approach that specifically targets deep feed-forward neural networks. Our quantization is based on mixed-integer linear programming (MILP) and leverages the unique structure of neural networks and effective over-approximations to make MILP optimization feasible. Our approach efficiently optimizes the number of bits needed to implement a network while guaranteeing a provided error bound. Our evaluation on existing embedded neural controller benchmarks shows that our optimization translates into precision assignments that mostly use fewer machine cycles when compiled to an FPGA with a commercial HLS compiler than code generated by (sound) state-of-the-art. Furthermore, our approach handles significantly more benchmarks substantially faster, especially for larger networks.<\/jats:p>","DOI":"10.1145\/3609118","type":"journal-article","created":{"date-parts":[[2023,9,9]],"date-time":"2023-09-09T13:33:18Z","timestamp":1694266398000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Sound Mixed Fixed-Point Quantization of Neural Networks"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8639-4116","authenticated-orcid":false,"given":"Debasmita","family":"Lohar","sequence":"first","affiliation":[{"name":"MPI-SWS, Saarland Informatics Campus, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9169-7031","authenticated-orcid":false,"given":"Clothilde","family":"Jeangoudoux","sequence":"additional","affiliation":[{"name":"MPI-SWS, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0702-5652","authenticated-orcid":false,"given":"Anastasia","family":"Volkova","sequence":"additional","affiliation":[{"name":"Nantes Universit\u00e9, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6848-3163","authenticated-orcid":false,"given":"Eva","family":"Darulova","sequence":"additional","affiliation":[{"name":"Uppsala University, Sweden"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,9,9]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"2020. 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Mathematical Programming151, 1 (2015).","journal-title":"Mathematical Programming"},{"key":"e_1_3_2_9_2","volume-title":"ISCAS","author":"Cantin M.-A.","year":"2001","unstructured":"M.-A. Cantin, Y. Savaria, D. Prodanos, and P. Lavoie. 2001. An automatic word length determination method. In ISCAS."},{"key":"e_1_3_2_10_2","volume-title":"DATE","author":"Carmichael Zachariah","year":"2019","unstructured":"Zachariah Carmichael, Hamed Fatemi Langroudi, Char Khazanov, Jeffrey Lillie, John L. Gustafson, and Dhireesha Kudithipudi. 2019. Deep positron: A deep neural network using the posit number system. In DATE."},{"key":"e_1_3_2_11_2","volume-title":"POPL","author":"Chiang Wei-Fan","year":"2017","unstructured":"Wei-Fan Chiang, Mark Baranowski, Ian Briggs, Alexey Solovyev, Ganesh Gopalakrishnan, and Zvonimir Rakamaric. 2017. Rigorous floating-point mixed-precision tuning. In POPL."},{"key":"e_1_3_2_12_2","volume-title":"ICCPS","author":"Darulova Eva","year":"2018","unstructured":"Eva Darulova, Einar Horn, and Saksham Sharma. 2018. Sound mixed-precision optimization with rewriting. In ICCPS."},{"key":"e_1_3_2_13_2","volume-title":"ARITH","author":"de Dinechin Florent","year":"2019","unstructured":"Florent de Dinechin. 2019. Reflections on 10 years of FloPoCo. In ARITH."},{"key":"e_1_3_2_14_2","volume-title":"ASAP","author":"de Dinechin Florent","year":"2014","unstructured":"Florent de Dinechin, Matei Istoan, and Abdelbassat Massouri. 2014. Sum-of-product architectures computing just right. In ASAP."},{"key":"e_1_3_2_15_2","unstructured":"Diego Manzanas Lopez and Patrick Musau. 2019. ARCH-2019 Github Repository. (2019).https:\/\/github.com\/verivital\/ARCH-2019"},{"key":"e_1_3_2_16_2","volume-title":"HSCC","author":"Dutta Souradeep","year":"2019","unstructured":"Souradeep Dutta, Xin Chen, and Sriram Sankaranarayanan. 2019. Reachability analysis for neural feedback systems using regressive polynomial rule inference. In HSCC."},{"issue":"16","key":"e_1_3_2_17_2","article-title":"Learning and verification of feedback control systems using feedforward neural networks","volume":"51","author":"Dutta Souradeep","year":"2018","unstructured":"Souradeep Dutta, Susmit Jha, Sriram Sankaranarayanan, and Ashish Tiwari. 2018. Learning and verification of feedback control systems using feedforward neural networks. IFAC-PapersOnLine51, 16 (2018).","journal-title":"IFAC-PapersOnLine"},{"key":"e_1_3_2_18_2","volume-title":"DATE","author":"Faraji S. Rasoul","year":"2019","unstructured":"S. Rasoul Faraji, M. Hassan Najafi, Bingzhe Li, David J. Lilja, and Kia Bazargan. 2019. Energy-efficient convolutional neural networks with deterministic bit-stream processing. In DATE."},{"issue":"11","key":"e_1_3_2_19_2","article-title":"Efficient approximate wordlength optimization","volume":"57","author":"Fiore Paul D.","year":"2008","unstructured":"Paul D. Fiore. 2008. Efficient approximate wordlength optimization. IEEE Trans. Computers 57, 11 (2008).","journal-title":"IEEE Trans. Computers"},{"key":"e_1_3_2_20_2","volume-title":"ICECS","author":"Franceschi Marta","year":"2018","unstructured":"Marta Franceschi, Alberto Nannarelli, and Maurizio Valle. 2018. Tunable floating-point for artificial neural networks. In ICECS."},{"key":"e_1_3_2_21_2","volume-title":"The SCIP Optimization Suite 7.0","author":"Gamrath Gerald","year":"2020","unstructured":"Gerald Gamrath, Daniel Anderson, Ksenia Bestuzheva, Wei-Kun Chen, Leon Eifler, Maxime Gasse, Patrick Gemander, Ambros Gleixner, Leona Gottwald, Katrin Halbig, Gregor Hendel, Christopher Hojny, Thorsten Koch, Pierre Le Bodic, Stephen J. Maher, Frederic Matter, Matthias Miltenberger, Erik M\u00fchmer, Benjamin M\u00fcller, Marc Pfetsch, Franziska Schl\u00f6sser, Felipe Serrano, Yuji Shinano, Christine Tawfik, Stefan Vigerske, Fabian Wegscheider, Dieter Weninger, Jakob Witzig. 2020. The SCIP Optimization Suite 7.0. Technical Report. http:\/\/www.optimization-online.org\/DB_HTML\/2020\/03\/7705.html"},{"key":"e_1_3_2_22_2","volume-title":"PLDI","author":"Gopinath Sridhar","year":"2019","unstructured":"Sridhar Gopinath, Nikhil Ghanathe, Vivek Seshadri, and Rahul Sharma. 2019. Compiling KB-sized machine learning models to tiny IoT devices. In PLDI."},{"key":"e_1_3_2_23_2","unstructured":"Dario Guidotti Stefano Demarchi Armando Tacchella and Luca Pulina. 2023. The Verification of Neural Networks Library (VNN-LIB). (2023).https:\/\/www.vnnlib.org"},{"key":"e_1_3_2_24_2","volume-title":"ICML","author":"Gupta Suyog","year":"2015","unstructured":"Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan. 2015. Deep learning with limited numerical precision. In ICML."},{"key":"e_1_3_2_25_2","unstructured":"Gurobi Optimization LLC. 2022. Gurobi Optimizer Reference Manual. (2022).https:\/\/www.gurobi.com"},{"key":"e_1_3_2_26_2","volume-title":"DATE","author":"Ha Van-Phu","year":"2021","unstructured":"Van-Phu Ha and Olivier Sentieys. 2021. Leveraging bayesian optimization to speed up automatic precision tuning. In DATE."},{"key":"e_1_3_2_27_2","volume-title":"APCCAS","author":"Habermann Tobias","year":"2022","unstructured":"Tobias Habermann, Jonas K\u00fchle, Martin Kumm, and Anastasia Volkova. 2022. Hardware-aware quantization for multiplierless neural network controllers. In APCCAS."},{"issue":"5","key":"e_1_3_2_28_2","article-title":"ReachNN: Reachability analysis of neural-network controlled systems","volume":"18","author":"Huang Chao","year":"2019","unstructured":"Chao Huang, Jiameng Fan, Wenchao Li, Xin Chen, and Qi Zhu. 2019. ReachNN: Reachability analysis of neural-network controlled systems. ACM Trans. Embed. Comput. Syst. 18, 5s (2019).","journal-title":"ACM Trans. Embed. Comput. Syst."},{"key":"e_1_3_2_29_2","article-title":"A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability","volume":"37","author":"Huang Xiaowei","year":"2020","unstructured":"Xiaowei Huang, Daniel Kroening, Wenjie Ruan, James Sharp, Youcheng Sun, Emese Thamo, Min Wu, and Xinping Yi. 2020. A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability. Comput. Sci. Rev.37 (2020).","journal-title":"Comput. Sci. Rev."},{"issue":"53","key":"e_1_3_2_30_2","article-title":"V12. 1: User\u2019s manual for CPLEX","volume":"46","author":"ILOG IBM","year":"2009","unstructured":"IBM ILOG. 2009. V12. 1: User\u2019s manual for CPLEX. 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Julian and Mykel J. Kochenderfer. 2019. A reachability method for verifying dynamical systems with deep neural network controllers. arXiv preprint arXiv:1903.00520 (2019).","journal-title":"arXiv preprint arXiv:1903.00520"},{"key":"e_1_3_2_37_2","volume-title":"NIPS","author":"K\u00f6ster Urs","year":"2017","unstructured":"Urs K\u00f6ster, Tristan J. Webb, Xin Wang, Marcel Nassar, Arjun K. Bansal, William H. Constable, Oguz H. Elibol, Scott Gray, Stewart Hall, Luke Hornof, Amir Khosrowshahi, Carey Kloss, Ruby J. Pai, Naveen Rao. 2017. Flexpoint: An adaptive numerical format for efficient training of deep neural networks. In NIPS."},{"key":"e_1_3_2_38_2","article-title":"Shiftry: RNN inference in 2KB of RAM","volume":"4","author":"Kumar Aayan","year":"2020","unstructured":"Aayan Kumar, Vivek Seshadri, and Rahul Sharma. 2020. Shiftry: RNN inference in 2KB of RAM. Proc. ACM Program. Lang.4, OOPSLA (2020).","journal-title":"Proc. ACM Program. Lang."},{"issue":"5","key":"e_1_3_2_39_2","article-title":"Optimal constant multiplication using integer linear programming","volume":"65","author":"Kumm Martin","year":"2018","unstructured":"Martin Kumm. 2018. Optimal constant multiplication using integer linear programming. IEEE Trans. Circuits Syst. II Express Briefs65-II, 5 (2018).","journal-title":"IEEE Trans. Circuits Syst. II Express Briefs"},{"key":"e_1_3_2_40_2","volume-title":"PMLR","author":"Lin Darryl","year":"2016","unstructured":"Darryl Lin, Sachin Talathi, and Sreekanth Annapureddy. 2016. Fixed point quantization of deep convolutional networks. In PMLR."},{"key":"e_1_3_2_41_2","doi-asserted-by":"crossref","DOI":"10.29007\/btv1","article-title":"Verification of closed-loop systems with neural network controllers","volume":"61","author":"Lopez Diego Manzanas","year":"2019","unstructured":"Diego Manzanas Lopez, Patrick Musau, Hoang-Dung Tran, and Taylor T. Johnson. 2019. Verification of closed-loop systems with neural network controllers. EPiC Series in Computing 61 (2019).","journal-title":"EPiC Series in Computing"},{"key":"e_1_3_2_42_2","doi-asserted-by":"crossref","DOI":"10.1109\/TCAD.2022.3197697","article-title":"An MILP encoding for efficient verification of quantized deep neural networks","author":"Mistry Samvid","year":"2022","unstructured":"Samvid Mistry, Indranil Saha, and Swarnendu Biswas. 2022. An MILP encoding for efficient verification of quantized deep neural networks. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (2022).","journal-title":"IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.5555\/1508122"},{"issue":"8","key":"e_1_3_2_44_2","article-title":"Machine learning at the network edge: A survey","volume":"54","author":"Murshed M. G. Sarwar","year":"2022","unstructured":"M. G. Sarwar Murshed, Christopher Murphy, Daqing Hou, Nazar Khan, Ganesh Ananthanarayanan, and Faraz Hussain. 2022. Machine learning at the network edge: A survey. ACM Comput. Surv. 54, 8 (2022).","journal-title":"ACM Comput. Surv."},{"key":"e_1_3_2_45_2","volume-title":"ISCA","author":"Park Eunhyeok","year":"2018","unstructured":"Eunhyeok Park, Dongyoung Kim, and Sungjoo Yoo. 2018. Energy-efficient neural network accelerator based on outlier-aware low-precision computation. In ISCA."},{"key":"e_1_3_2_46_2","volume-title":"ICECS","author":"Pradeep Kathirgamaraja","year":"2018","unstructured":"Kathirgamaraja Pradeep, Kamalakkannan Kamalavasan, Ratnasegar Natheesan, and Ajith Pasqual. 2018. EdgeNet: SqueezeNet like convolution neural network on embedded FPGA. In ICECS."},{"key":"e_1_3_2_47_2","volume-title":"ISCAS","author":"Garcia Anastasia Volkova, Remi","year":"2022","unstructured":"Anastasia Volkova, Remi Garcia, and Martin Kumm. 2022. Truncated multiple constant multiplication with minimal number of full adders. In ISCAS."},{"key":"e_1_3_2_48_2","volume-title":"ISCA","author":"Sharma Hardik","year":"2018","unstructured":"Hardik Sharma, Jongse Park, Naveen Suda, Liangzhen Lai, Benson Chau, Vikas Chandra, and Hadi Esmaeilzadeh. 2018. Bit fusion: Bit-level dynamically composable architecture for accelerating deep neural network. In ISCA."},{"key":"e_1_3_2_49_2","volume-title":"ISCA","author":"Song Zhuoran","year":"2020","unstructured":"Zhuoran Song, Bangqi Fu, Feiyang Wu, Zhaoming Jiang, Li Jiang, Naifeng Jing, and Xiaoyao Liang. 2020. DRQ: Dynamic region-based quantization for deep neural network acceleration. In ISCA."},{"key":"e_1_3_2_50_2","volume-title":"HSCC","author":"Sun Xiaowu","year":"2019","unstructured":"Xiaowu Sun, Haitham Khedr, and Yasser Shoukry. 2019. Formal verification of neural network controlled autonomous systems. 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(2021). https:\/\/www.xilinx.com"},{"issue":"2","key":"e_1_3_2_54_2","article-title":"Deep neural network approximation for custom hardware: Where we\u2019ve been, where we\u2019re going","volume":"52","author":"Wang Erwei","year":"2019","unstructured":"Erwei Wang, James J. Davis, Ruizhe Zhao, Ho-Cheung Ng, Xinyu Niu, Wayne Luk, Peter Y. K. Cheung, and George A. Constantinides. 2019. Deep neural network approximation for custom hardware: Where we\u2019ve been, where we\u2019re going. Comput. Surveys 52, 2 (2019).","journal-title":"Comput. Surveys"},{"key":"e_1_3_2_55_2","article-title":"Output range analysis for feed-forward deep neural networks via linear programming","author":"Xu Zhiwu","year":"2022","unstructured":"Zhiwu Xu, Yazheng Liu, Shengchao Qin, and Zhong Ming. 2022. Output range analysis for feed-forward deep neural networks via linear programming. 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