{"id":{"repo_id":"gatech","oai_identifier":"oai:repository.gatech.edu:1853/64650"},"canonical_url":"https://search.dev.ndltd.org/etd/gatech/oai:repository.gatech.edu:1853/64650","repository":{"repo_id":"gatech","name":"Georgia Tech","base_url":"https://repository.gatech.edu/server/oai/request"},"display":{"title":"Investigating Opportunities and Challenges in Modeling and Designing Scale-Out DNN Accelerators","abstract":"The rapid growth of deep learning used in practical applications such as speech recognition, computer vision, natural language processing, robotics, any many other fields has opened the gate to new technology possibilities. Unfortunately, traditional hardware systems are being stretched to the maximum to accommodate the intense workloads presented by state-of-the-art deep learning processes in a time when transistor technology is not scaling. To serve the demand for better computational power and more specialized computations, specialized hardware needs to be developed that provides better latency and bandwidth specifications for various demanding applications. The trend in the semi-conductor industry is to move towards heterogenous System-OnChip (SoC) thereby choosing application specific performance vs. generality seen in most CPU architectures today. In most situations, hardware engineers are left to construct systems that serve the needs of various applications, often needing to predict the use-cases of the system. As with any field, the ability to predict and act on the future innovation trends of the industry is the difference between success and failure. A novel simulator for the design of convolutional neural network accelerators is presented and described in detail named SCALE-Sim (Systolic CNN Accelerator Simulator). The simulator is available as an open-sourced repository and has 2 primary use-cases in which computer architects can extract significant results. The first use-case is for system designers who would like to integrate an existing DNN accelerator architecture into a larger SoC and would be interested in system-level characterization results. The second use-case is for an accelerator architect who would like to use the tool to explore the accelerator design space by sweeping through design parameters.","abstract_html":"The rapid growth of deep learning used in practical applications such as speech recognition, computer vision, natural language processing, robotics, any many other fields has opened the gate to new technology possibilities. Unfortunately, traditional hardware systems are being stretched to the maximum to accommodate the intense workloads presented by state-of-the-art deep learning processes in a time when transistor technology is not scaling. To serve the demand for better computational power and more specialized computations, specialized hardware needs to be developed that provides better latency and bandwidth specifications for various demanding applications. The trend in the semi-conductor industry is to move towards heterogenous System-OnChip (SoC) thereby choosing application specific performance vs. generality seen in most CPU architectures today. In most situations, hardware engineers are left to construct systems that serve the needs of various applications, often needing to predict the use-cases of the system. As with any field, the ability to predict and act on the future innovation trends of the industry is the difference between success and failure. A novel simulator for the design of convolutional neural network accelerators is presented and described in detail named SCALE-Sim (Systolic CNN Accelerator Simulator). The simulator is available as an open-sourced repository and has 2 primary use-cases in which computer architects can extract significant results. The first use-case is for system designers who would like to integrate an existing DNN accelerator architecture into a larger SoC and would be interested in system-level characterization results. The second use-case is for an accelerator architect who would like to use the tool to explore the accelerator design space by sweeping through design parameters.","abstract_has_math":false,"creators":["Nadella, Vineet"],"institution":"Georgia Institute of Technology","degree_name":null,"degree_level":"Masters","degree_discipline":null,"degree_department":"Electrical and Computer Engineering","school":null,"contributors":[],"advisors":["Krishna, Tushar"],"committee_chairs":[],"committee_members":["Krishna, Tushar","Mukhopadhyay, Saibal","Daglis, Alexandros"],"year":2020,"date_issued":"2020-04-28","date_published":"2020-04-28","updated_at":"2026-07-27T19:51:20Z","subjects":["Deep Learning, Computer Architecture"],"languages":["en_US"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1853/64650","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Krishna, Tushar"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Krishna, Tushar","Mukhopadhyay, Saibal","Daglis, Alexandros"]},{"key":"dc:contributor.department","label":"Department","values":["Electrical and Computer Engineering"]},{"key":"dc:creator","label":"Author","values":["Nadella, Vineet"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-06-10T16:48:38Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-06-10T16:48:38Z"]},{"key":"dc:date.issued","label":"Date","values":["2020-04-28"]},{"key":"dc:publisher","label":"Institution","values":["Georgia Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Text"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Deep Learning, Computer Architecture"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en_US"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1853/64650"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The rapid growth of deep learning used in practical applications such as speech recognition, computer vision, natural language processing, robotics, any many other fields has opened the gate to new technology possibilities. Unfortunately, traditional hardware systems are being stretched to the maximum to accommodate the intense workloads presented by state-of-the-art deep learning processes in a time when transistor technology is not scaling. To serve the demand for better computational power and more specialized computations, specialized hardware needs to be developed that provides better latency and bandwidth specifications for various demanding applications. The trend in the semi-conductor industry is to move towards heterogenous System-OnChip (SoC) thereby choosing application specific performance vs. generality seen in most CPU architectures today. In most situations, hardware engineers are left to construct systems that serve the needs of various applications, often needing to predict the use-cases of the system. As with any field, the ability to predict and act on the future innovation trends of the industry is the difference between success and failure. A novel simulator for the design of convolutional neural network accelerators is presented and described in detail named SCALE-Sim (Systolic CNN Accelerator Simulator). The simulator is available as an open-sourced repository and has 2 primary use-cases in which computer architects can extract significant results. The first use-case is for system designers who would like to integrate an existing DNN accelerator architecture into a larger SoC and would be interested in system-level characterization results. The second use-case is for an accelerator architect who would like to use the tool to explore the accelerator design space by sweeping through design parameters."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.S."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Investigating Opportunities and Challenges in Modeling and Designing Scale-Out DNN Accelerators"]}]}],"canonical_facts":{"dc:contributor.advisor":["Krishna, Tushar"],"dc:contributor.committeemember":["Krishna, Tushar","Mukhopadhyay, Saibal","Daglis, Alexandros"],"dc:contributor.department":["Electrical and Computer Engineering"],"dc:creator":["Nadella, Vineet"],"dc:date.accessioned":["2021-06-10T16:48:38Z"],"dc:date.available":["2021-06-10T16:48:38Z"],"dc:date.issued":["2020-04-28"],"dc:description.abstract":["The rapid growth of deep learning used in practical applications such as speech recognition, computer vision, natural language processing, robotics, any many other fields has opened the gate to new technology possibilities. Unfortunately, traditional hardware systems are being stretched to the maximum to accommodate the intense workloads presented by state-of-the-art deep learning processes in a time when transistor technology is not scaling. To serve the demand for better computational power and more specialized computations, specialized hardware needs to be developed that provides better latency and bandwidth specifications for various demanding applications. The trend in the semi-conductor industry is to move towards heterogenous System-OnChip (SoC) thereby choosing application specific performance vs. generality seen in most CPU architectures today. In most situations, hardware engineers are left to construct systems that serve the needs of various applications, often needing to predict the use-cases of the system. As with any field, the ability to predict and act on the future innovation trends of the industry is the difference between success and failure. A novel simulator for the design of convolutional neural network accelerators is presented and described in detail named SCALE-Sim (Systolic CNN Accelerator Simulator). The simulator is available as an open-sourced repository and has 2 primary use-cases in which computer architects can extract significant results. The first use-case is for system designers who would like to integrate an existing DNN accelerator architecture into a larger SoC and would be interested in system-level characterization results. The second use-case is for an accelerator architect who would like to use the tool to explore the accelerator design space by sweeping through design parameters."],"dc:description.degree":["M.S."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["http://hdl.handle.net/1853/64650"],"dc:language.iso":["en_US"],"dc:publisher":["Georgia Institute of Technology"],"dc:subject":["Deep Learning, Computer Architecture"],"dc:title":["Investigating Opportunities and Challenges in Modeling and Designing Scale-Out DNN Accelerators"],"dc:type":["Text"],"thesis:degree_level":["Masters"]},"updated_at":"2026-07-27T19:51:20Z"}