Please use this identifier to cite or link to this item: https://doi.org/10.1109/CICC53496.2022.9772786
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dc.titleDDPMnet: All-Digital Pulse Density-Based DNN Architecture with 228 Gate Equivalents/MAC Unit, 28-TOPS/W and 1.5-TOPS/mm2 in 40nm
dc.contributor.authorAnimesh Gupta
dc.contributor.authorViveka Konandur
dc.contributor.authorThoithoi Salam
dc.contributor.authorSaurabh Jain
dc.contributor.authorOrazio Aiello
dc.contributor.authorPaolo Crovettii
dc.contributor.authorMassimo Alioto
dc.date.accessioned2023-02-14T09:58:02Z
dc.date.available2023-02-14T09:58:02Z
dc.date.issued2022-04-24
dc.identifier.citationAnimesh Gupta, Viveka Konandur, Thoithoi Salam, Saurabh Jain, Orazio Aiello, Paolo Crovettii, Massimo Alioto (2022-04-24). DDPMnet: All-Digital Pulse Density-Based DNN Architecture with 228 Gate Equivalents/MAC Unit, 28-TOPS/W and 1.5-TOPS/mm2 in 40nm. DDPMnet: All-Digital Pulse Density-Based DNN Architecture with 228 Gate Equivalents/MAC Unit, 28-TOPS/W and 1.5-TOPS/mm2 in 40nm IEEE Custom Integrated Circuits Conference (CICC). ScholarBank@NUS Repository. https://doi.org/10.1109/CICC53496.2022.9772786
dc.identifier.isbn978-1-6654-0756-4
dc.identifier.issn2152-3630
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/237282
dc.description.abstractRelentless advances in DNN accelerator energy and area efficiency are demanded in low-cost edge devices [1]–[8]. Both directly benefit from the reduction in the complexity of MAC units (neurons), thanks to the reduction in area and energy of computations and the interconnect fabric. Unfortunately, such area and energy cost per neuron further increases in practical cases where flexibility is needed (e.g., precision scaling), ultimately limiting cost and power reductions. In this work, the all-digital DDPMnet architecture for DNN acceleration based on a pulse density data representation is introduced to reduce the gate count/MAC unit from the thousand range to few hundreds (Fig. 1). The proposed architecture removes any arithmetic block from MAC units (e.g., multipliers), while retaining the advantages of standard cell based design.
dc.description.urihttps://ieeexplore.ieee.org/abstract/document/9772786
dc.language.isoen
dc.publisherIEEE
dc.rightsCC0 1.0 Universal
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/
dc.subjectdata structures , neural nets
dc.typeConference Paper
dc.contributor.departmentELECTRICAL AND COMPUTER ENGINEERING
dc.description.doi10.1109/CICC53496.2022.9772786
dc.description.sourcetitleDDPMnet: All-Digital Pulse Density-Based DNN Architecture with 228 Gate Equivalents/MAC Unit, 28-TOPS/W and 1.5-TOPS/mm2 in 40nm
dc.description.volumeIEEE Custom Integrated Circuits Conference (CICC)
dc.published.statePublished
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