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Syst."],"published-print":{"date-parts":[[2019,10,31]]},"abstract":"<jats:p>\n            Brain-inspired hyperdimensional (HD) computing models neural activity patterns of the very size of the brain\u2019s circuits with points of a hyperdimensional space, that is, with\n            <jats:italic>hypervectors<\/jats:italic>\n            . Hypervectors are\n            <jats:italic>D<\/jats:italic>\n            -dimensional (pseudo)random vectors with independent and identically distributed (i.i.d.) components constituting ultra-wide holographic words:\n            <jats:italic>D<\/jats:italic>\n            =10,000 bits, for instance. At its very core, HD computing manipulates a set of seed hypervectors to build composite hypervectors representing objects of interest. It demands memory optimizations with simple operations for an efficient hardware realization. In this article, we propose hardware techniques for optimizations of HD computing, in a synthesizable open-source VHDL library, to enable co-located implementation of both learning and classification tasks on only a small portion of Xilinx UltraScale FPGAs: (1) We propose simple logical operations to\n            <jats:italic>rematerialize<\/jats:italic>\n            the hypervectors on the fly rather than loading them from memory. These operations massively reduce the memory footprint by directly computing the composite hypervectors whose individual seed hypervectors do not need to be stored in memory. (2) Bundling a series of hypervectors over time requires a multibit counter per every hypervector component. We instead propose a binarized\n            <jats:italic>back-to-back<\/jats:italic>\n            bundling without requiring any counters. This truly enables on-chip learning with minimal resources as every hypervector component remains binary over the course of training to avoid otherwise multibit components. (3) For every classification event, an associative memory is in charge of finding the closest match between a set of learned hypervectors and a query hypervector by using a distance metric. This operator is proportional to hypervector dimension (\n            <jats:italic>D<\/jats:italic>\n            ), and hence may take O(\n            <jats:italic>D<\/jats:italic>\n            ) cycles per classification event. Accordingly, we significantly improve the throughput of classification by proposing associative memories that steadily reduce the latency of classification to the extreme of a single cycle. (4) We perform a design space exploration incorporating the proposed techniques on FPGAs for a wearable biosignal processing application as a case study. Our techniques achieve up to 2.39\u00d7 area saving, or 2,337\u00d7 throughput improvement. The Pareto optimal HD architecture is mapped on only 18,340 configurable logic blocks (CLBs) to learn and classify five hand gestures using four electromyography sensors.\n          <\/jats:p>","DOI":"10.1145\/3314326","type":"journal-article","created":{"date-parts":[[2019,10,10]],"date-time":"2019-10-10T13:13:05Z","timestamp":1570713185000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":68,"title":["Hardware Optimizations of Dense Binary Hyperdimensional Computing: Rematerialization of Hypervectors, Binarized Bundling, and Combinational Associative Memory"],"prefix":"10.1145","volume":"15","author":[{"given":"Manuel","family":"Schmuck","sequence":"first","affiliation":[{"name":"ETH Z\u00fcrich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Benini","sequence":"additional","affiliation":[{"name":"ETH Z\u00fcrich and Universit\u00e0 di Bologna, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abbas","family":"Rahimi","sequence":"additional","affiliation":[{"name":"ETH Z\u00fcrich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,10,10]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/BIOCAS.2018.8584751"},{"key":"e_1_2_1_2_1","first-page":"11","article-title":"A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand","volume":"53","author":"Chu J. 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Fateme Rasti Najafabadi, Abbas Rahimi, Pentti Kanerva, and Jan M. Rabaey. 2016. Hyperdimensional computing for text classification. In Proceedings of the Design, Automation Test in Europe Conference Exhibition (DATE\u201916). Retrieved from https:\/\/www.date-conference.com\/system\/files\/file\/date16\/ubooth\/37923.pdf."},{"key":"e_1_2_1_24_1","volume-title":"Proceedings of the International Conference on Intelligent Robots and Systems (IROS\u201916) and the Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics.","author":"Neubert P.","unstructured":"P. Neubert , S. Schubert , and P. Protzel . 2016. Learning vector symbolic architectures for reactive robot behaviours . In Proceedings of the International Conference on Intelligent Robots and Systems (IROS\u201916) and the Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics. P. Neubert, S. Schubert, and P. Protzel. 2016. Learning vector symbolic architectures for reactive robot behaviours. In Proceedings of the International Conference on Intelligent Robots and Systems (IROS\u201916) and the Workshop on Machine Learning Methods for High-Level Cognitive Capabilities in Robotics."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/72.377968"},{"key":"e_1_2_1_26_1","volume-title":"Holographic Reduced Representations","author":"Plate T. A.","unstructured":"T. A. Plate . 2003. Holographic Reduced Representations . CLSI Publications . T. A. Plate. 2003. Holographic Reduced Representations. CLSI Publications."},{"key":"e_1_2_1_27_1","first-page":"1","article-title":"Binary vectors for fast distance and similarity estimation","volume":"53","author":"Rachkovskij D. A.","year":"2017","unstructured":"D. A. Rachkovskij . 2017 . Binary vectors for fast distance and similarity estimation . Cybernet. Syst. Anal. 53 , 1 (Jan. 2017), 138--156. 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Hyperdimensional computing for noninvasive brain--computer interfaces: Blind and one-shot classification of EEG error-related potentials . In Proceedings of the 10th ACM\/EAI International Conference on Bio-inspired Information and Communications Technologies (BICT\u201917) ( 2017 ). Abbas Rahimi, Pentti Kanerva, Jos\u00e9 del R. Mill\u00e1n, and Jan M. Rabaey. 2017. Hyperdimensional computing for noninvasive brain--computer interfaces: Blind and one-shot classification of EEG error-related potentials. In Proceedings of the 10th ACM\/EAI International Conference on Bio-inspired Information and Communications Technologies (BICT\u201917) (2017)."},{"key":"e_1_2_1_32_1","volume-title":"Proceedings of the IEEE\/ACM International Symposium on Low Power Electronics and Design (ISLPED\u201916)","author":"Rahimi Abbas","unstructured":"Abbas Rahimi , Pentti Kanerva , and Jan M. Rabaey . 2016. A robust and energy efficient classifier using brain-inspired hyperdimensional computing . In Proceedings of the IEEE\/ACM International Symposium on Low Power Electronics and Design (ISLPED\u201916) . Abbas Rahimi, Pentti Kanerva, and Jan M. Rabaey. 2016. A robust and energy efficient classifier using brain-inspired hyperdimensional computing. In Proceedings of the IEEE\/ACM International Symposium on Low Power Electronics and Design (ISLPED\u201916)."},{"key":"e_1_2_1_33_1","volume-title":"Rabaey","author":"Rahimi Abbas","year":"2017","unstructured":"Abbas Rahimi , Artiom Tchouprina , Pentti Kanerva , Jos\u00e9 del R. Mill\u00e1n , and Jan M . Rabaey . 2017 . Hyperdimensional computing for blind and one-shot classification of EEG error-related potentials. Mobile Netw. Appl . (Oct. 2017). DOI:https:\/\/doi.org\/10.1007\/s11036-017-0942-6 10.1007\/s11036-017-0942-6 Abbas Rahimi, Artiom Tchouprina, Pentti Kanerva, Jos\u00e9 del R. Mill\u00e1n, and Jan M. Rabaey. 2017. Hyperdimensional computing for blind and one-shot classification of EEG error-related potentials. 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DOI:https:\/\/doi.org\/10.1109\/TNNLS.2015.2462721 10.1109\/TNNLS.2015.2462721 O. R\u00e4s\u00e4nen and J. Saarinen. 2015. Sequence prediction with sparse distributed hyperdimensional coding applied to the analysis of mobile phone use patterns. IEEE Trans. Neural Netw. Learn. Syst. 99 (2015), 1878--1889. DOI:https:\/\/doi.org\/10.1109\/TNNLS.2015.2462721","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"e_1_2_1_37_1","volume-title":"Proceedings of the International Symposium on Signals, Systems and Electronics. 343--346","author":"Santoro R.","year":"2007","unstructured":"R. Santoro , S. Roy , and O. Sentieys . 2007. Search for optimal five-neighbor FPGA-based cellular automata random number generators . In Proceedings of the International Symposium on Signals, Systems and Electronics. 343--346 . DOI:https:\/\/doi.org\/10.1109\/ISSSE. 2007 .4294483 10.1109\/ISSSE.2007.4294483 R. Santoro, S. Roy, and O. Sentieys. 2007. Search for optimal five-neighbor FPGA-based cellular automata random number generators. In Proceedings of the International Symposium on Signals, Systems and Electronics. 343--346. DOI:https:\/\/doi.org\/10.1109\/ISSSE.2007.4294483"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/0196-8858(86)90028-X"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2018.2870560"},{"key":"e_1_2_1_40_1","volume-title":"Proceedings of the IEEE International Solid-State Circuits Conference (ISSCC\u201918)","author":"Wu T. F.","year":"2018","unstructured":"T. F. Wu , H. Li , P. C. Huang , A. Rahimi , J. M. Rabaey , H. S. P. Wong , M. M. Shulaker , and S. Mitra . 2018. Brain-inspired computing exploiting carbon nanotube FETs and resistive RAM: Hyperdimensional computing case study . In Proceedings of the IEEE International Solid-State Circuits Conference (ISSCC\u201918) . 492--494. 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