University of Illinois at Chicago
A_Machine_Learning_Pipeline_Stage_for_Adaptive_Frequency_Adjustment.pdf (4.1 MB)

A Machine Learning Pipeline Stage for Adaptive Frequency Adjustment

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journal contribution
posted on 2023-07-20, 19:09 authored by Arash Fouman Ajirlou, Inna Partin-Vaisband
A machine learning (ML) design framework is proposed for adaptively adjusting clock frequency based on propagation delay of individual instructions. A random forest model is trained to classify propagation delays in real time, utilizing current operation type, current operands, and computation history as ML features. The trained model is implemented in Verilog as an additional pipeline stage within a baseline processor. The modified system is experimentally tested at the gate level in 45 nm CMOS technology, exhibiting a speedup of 70% and energy reduction of 30% with coarse-grained ML classification. A speedup of 89% is demonstrated with finer granularities with 15.5% reduction in energy consumption.



Ajirlou, A. F.Partin-Vaisband, I. (2020). A Machine Learning Pipeline Stage for Adaptive Frequency Adjustment.




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