Animesh Gupta, V. Rajanna, Thoithoi Salam, Saurabh Jain, O. Aiello, P. Crovetti, M. Alioto
{"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","authors":"Animesh Gupta, V. Rajanna, Thoithoi Salam, Saurabh Jain, O. Aiello, P. Crovetti, M. Alioto","doi":"10.1109/CICC53496.2022.9772786","DOIUrl":null,"url":null,"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.","PeriodicalId":415990,"journal":{"name":"2022 IEEE Custom Integrated Circuits Conference (CICC)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Custom Integrated Circuits Conference (CICC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CICC53496.2022.9772786","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
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.