{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T17:17:14Z","timestamp":1789233434171,"version":"build-2803163510"},"reference-count":42,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2020,8,26]],"date-time":"2020-08-26T00:00:00Z","timestamp":1598400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSF","award":["CCF-1629450 and SHF-1619127"],"award-info":[{"award-number":["CCF-1629450 and SHF-1619127"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["J. Emerg. Technol. Comput. Syst."],"published-print":{"date-parts":[[2020,10,31]]},"abstract":"<jats:p>\n            We propose a novel architecture to enable low-power, complex on-node data processing, for the next generation of sensors for the internet of things (IoT), smartdust, or edge intelligence. Our architecture combines near-analog-memory-computing (NAM) and asynchronous-computing-with-streams (ACS), eliminating the need for ADCs. ACS enables ultra-low power, massive computational resources required to execute on-node complex Machine Learning (ML) algorithms; while NAM addresses the memory-wall that represents a common bottleneck for ML and other complex functions. In ACS an analog value is mapped to an asynchronous stream that can take one of two logic levels (\n            <jats:italic>v<\/jats:italic>\n            <jats:sub>\n              <jats:italic>h<\/jats:italic>\n            <\/jats:sub>\n            ,\n            <jats:italic>v<\/jats:italic>\n            <jats:sub>\n              <jats:italic>l<\/jats:italic>\n            <\/jats:sub>\n            ). This stream-based data representation enables area\/power-efficient computing units such as a multiplier implemented as an AND gate yielding savings in power of \u223c90% compared to digital approaches. The generation of streams for NAM and ACS in a brute force manner, using analog-to-digital-converters (ADCs) and digital-to-streams-converters, would sky-rocket the power-latency-energy cost making the approach impractical. Our NAM-ACS architecture eliminates expensive conversions, enabling an end-to-end processing on asynchronous streams data-path. We tailor the NAM-ACS architecture for random forest (RaF), an ML algorithm, chosen for its ability to classify using a reduced number of features. Simulations show that our NAM-ACS architecture enables 75% of savings in power compared with a single ADC, obtaining a classification accuracy of 85% using an RaF-inspired algorithm.\n          <\/jats:p>","DOI":"10.1145\/3404975","type":"journal-article","created":{"date-parts":[[2020,8,26]],"date-time":"2020-08-26T10:06:10Z","timestamp":1598436370000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["Towards on-node Machine Learning for Ultra-low-power Sensors Using Asynchronous \u03a3 \u0394 Streams"],"prefix":"10.1145","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4377-7496","authenticated-orcid":false,"given":"Patricia","family":"Gonzalez-Guerrero","sequence":"first","affiliation":[{"name":"University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tommy","family":"Tracy II","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2374-3953","authenticated-orcid":false,"given":"Xinfei","family":"Guo","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rahul","family":"Sreekumar","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marzieh","family":"Lenjani","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Skadron","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mircea R.","family":"Stan","sequence":"additional","affiliation":[{"name":"University of Virginia, Charlottesville, Virginia, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2020,8,26]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of the 50th ACM\/EDAC\/IEEE Design Automation Conference (DAC\u201913)","author":"\u00a0al A. 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