Please use this identifier to cite or link to this item:
https://doi.org/10.1109/CICC53496.2022.9772786
DC Field | Value | |
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dc.title | DDPMnet: 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.author | Animesh Gupta | |
dc.contributor.author | Viveka Konandur | |
dc.contributor.author | Thoithoi Salam | |
dc.contributor.author | Saurabh Jain | |
dc.contributor.author | Orazio Aiello | |
dc.contributor.author | Paolo Crovettii | |
dc.contributor.author | Massimo Alioto | |
dc.date.accessioned | 2023-02-14T09:58:02Z | |
dc.date.available | 2023-02-14T09:58:02Z | |
dc.date.issued | 2022-04-24 | |
dc.identifier.citation | Animesh 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.isbn | 978-1-6654-0756-4 | |
dc.identifier.issn | 2152-3630 | |
dc.identifier.uri | https://scholarbank.nus.edu.sg/handle/10635/237282 | |
dc.description.abstract | Relentless 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.uri | https://ieeexplore.ieee.org/abstract/document/9772786 | |
dc.language.iso | en | |
dc.publisher | IEEE | |
dc.rights | CC0 1.0 Universal | |
dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | |
dc.subject | data structures , neural nets | |
dc.type | Conference Paper | |
dc.contributor.department | ELECTRICAL AND COMPUTER ENGINEERING | |
dc.description.doi | 10.1109/CICC53496.2022.9772786 | |
dc.description.sourcetitle | DDPMnet: 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.volume | IEEE Custom Integrated Circuits Conference (CICC) | |
dc.published.state | Published | |
Appears in Collections: | Staff Publications Elements Students Publications |
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File | Description | Size | Format | Access Settings | Version | |
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ieee_non_blind_ddpmnet_2022.pdf | Manuscript | 365.72 kB | Adobe PDF | CLOSED | Published | |
19_5_Gupta.pdf | Presentation | 1.4 MB | Adobe PDF | CLOSED | Published |
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