Energy-efficient Hardware Acceleration of Shallow Machine Learning Applications

Research output: Chapter in Book/Report/Conference proceedingConference contribution


ML accelerators have largely focused on building general platforms for deep neural networks (DNNs), but less so on shallow machine learning (SML) algorithms. This paper proposes Axiline, a compact, configurable, template-based generator for SML hardware acceleration. Axiline identifies computational kernels as templates that are common to these algorithms and builds a pipelined accelerator for efficient execution. The dataflow graphs of individual ML instances, with different data dimensions, are mapped to the pipeline stages and then optimized by customized algorithms. The approach generates energy-efficient hardware for training and inference of various ML algorithms, as demonstrated with post-layout FPGA and ASIC results.

Original languageEnglish (US)
Title of host publication2023 Design, Automation and Test in Europe Conference and Exhibition, DATE 2023 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9783981926378
StatePublished - 2023
Event2023 Design, Automation and Test in Europe Conference and Exhibition, DATE 2023 - Antwerp, Belgium
Duration: Apr 17 2023Apr 19 2023

Publication series

NameProceedings -Design, Automation and Test in Europe, DATE
ISSN (Print)1530-1591


Conference2023 Design, Automation and Test in Europe Conference and Exhibition, DATE 2023

Bibliographical note

Funding Information:
This material is based on research sponsored in part by Air Force Research Laboratory (AFRL) and Defense Advanced Research Projects Agency (DARPA) under agreement number FA8650-20-2-7009. The U. S. government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of AFRL, DARPA, or the U. S. government.

Publisher Copyright:
© 2023 EDAA.


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