{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/88010"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/88010","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A dot product kernel using rapidly switched analog circuit","abstract":"In a world driven by technology and hand-held devices, there is ubiquitous demand for high-performance, low-energy processing engines. In this thesis, we present rapidly switched analog circuit (RSAC), a new circuit architecture, to implement an energy-efficient mixed-signal dot product (DP) kernel for machine learning and signal processing applications. RSAC operates by fast switching the analog inputs to the output via variable width digital pulses. A description of the different components of RSAC, along with a detailed accuracy and energy consumption analysis is presented. We show two RSAC designs that span the different design options and technology nodes. Simulations for the first design in a 130 nm process show energy savings of 19x to 32x compared to a digital implementation for signal-to-quantization-noise ratios (SQNRs) of 30 dB to 24 dB, respectively. Simulations for the second design in a 28 nm FDSOI process show energy savings of 15.7x, 4x, 2.1x compared to a digital implementation running at the same sampling frequency for SQNRs of 8 dB, 14 dB and 20 dB, respectively. Finally, we present the design of an emotion recognition system composed solely of SAC-based dot-products. Based on the behavioral and energy models developed in this thesis, we obtain energy savings of 45% and 49% compared to a digital implementation for average probabilities of error of 0.23 and 0.07, running at frequencies of 1.87 MHz and 1.7 MHz, respectively.","abstract_html":"In a world driven by technology and hand-held devices, there is ubiquitous demand for high-performance, low-energy processing engines. In this thesis, we present rapidly switched analog circuit (RSAC), a new circuit architecture, to implement an energy-efficient mixed-signal dot product (DP) kernel for machine learning and signal processing applications. RSAC operates by fast switching the analog inputs to the output via variable width digital pulses. A description of the different components of RSAC, along with a detailed accuracy and energy consumption analysis is presented. We show two RSAC designs that span the different design options and technology nodes. Simulations for the first design in a 130 nm process show energy savings of 19x to 32x compared to a digital implementation for signal-to-quantization-noise ratios (SQNRs) of 30 dB to 24 dB, respectively. Simulations for the second design in a 28 nm FDSOI process show energy savings of 15.7x, 4x, 2.1x compared to a digital implementation running at the same sampling frequency for SQNRs of 8 dB, 14 dB and 20 dB, respectively. Finally, we present the design of an emotion recognition system composed solely of SAC-based dot-products. Based on the behavioral and energy models developed in this thesis, we obtain energy savings of 45% and 49% compared to a digital implementation for average probabilities of error of 0.23 and 0.07, running at frequencies of 1.87 MHz and 1.7 MHz, respectively.","abstract_has_math":false,"creators":["Nahlus, Ihab"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engineering","degree_department":null,"school":null,"contributors":["Shanbhag, Naresh R."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-29T20:38:09Z","date_published":"2015-09-29T20:38:09Z","updated_at":"2026-07-22T22:26:31Z","subjects":["low energy","mixed-signal","dot product"],"languages":["en"],"rights":["Copyright 2015 Ihab Nahlus"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/88010","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Shanbhag, Naresh R."]},{"key":"dc:creator","label":"Author","values":["Nahlus, Ihab"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2015-09-29T20:38:09Z","2015-08","2015-07-13","2015-8"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["low energy","mixed-signal","dot product"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2015 Ihab Nahlus"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/88010"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In a world driven by technology and hand-held devices, there is ubiquitous demand for high-performance, low-energy processing engines. In this thesis, we present rapidly switched analog circuit (RSAC), a new circuit architecture, to implement an energy-efficient mixed-signal dot product (DP) kernel for machine learning and signal processing applications. RSAC operates by fast switching the analog inputs to the output via variable width digital pulses. A description of the different components of RSAC, along with a detailed accuracy and energy consumption analysis is presented. We show two RSAC designs that span the different design options and technology nodes. Simulations for the first design in a 130 nm process show energy savings of 19x to 32x compared to a digital implementation for signal-to-quantization-noise ratios (SQNRs) of 30 dB to 24 dB, respectively. Simulations for the second design in a 28 nm FDSOI process show energy savings of 15.7x, 4x, 2.1x compared to a digital implementation running at the same sampling frequency for SQNRs of 8 dB, 14 dB and 20 dB, respectively. Finally, we present the design of an emotion recognition system composed solely of SAC-based dot-products. Based on the behavioral and energy models developed in this thesis, we obtain energy savings of 45% and 49% compared to a digital implementation for average probabilities of error of 0.23 and 0.07, running at frequencies of 1.87 MHz and 1.7 MHz, respectively.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2015-09-29 without embargo terms","The student, Ihab Nahlus, accepted the attached license on 2015-07-08 at 13:43.","The student, Ihab Nahlus, submitted this Thesis for approval on 2015-07-08 at 13:55.","This Thesis was approved for publication on 2015-07-13 at 15:24.","DSpace SAF Submission Ingestion Package generated from Vireo submission #8374 on 2015-09-29 at 13:22:16","Made available in DSpace on 2015-09-29T20:38:09Z (GMT). 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RSAC operates by fast switching the analog inputs to the output via variable width digital pulses. A description of the different components of RSAC, along with a detailed accuracy and energy consumption analysis is presented. We show two RSAC designs that span the different design options and technology nodes. Simulations for the first design in a 130 nm process show energy savings of 19x to 32x compared to a digital implementation for signal-to-quantization-noise ratios (SQNRs) of 30 dB to 24 dB, respectively. Simulations for the second design in a 28 nm FDSOI process show energy savings of 15.7x, 4x, 2.1x compared to a digital implementation running at the same sampling frequency for SQNRs of 8 dB, 14 dB and 20 dB, respectively. Finally, we present the design of an emotion recognition system composed solely of SAC-based dot-products. 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