Post-Silicon Customization Using Deep Neural Networks

Kevin Weston, Vahid Janfaza, Abhishek Taur, Dhara Mungra, Arnav Kansal, Mohamed Zahran, Abdullah Muzahid

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


Dynamically customizing processor architecture after fabrication, also known as Post-Silicon Customization (PSC) is effective in balancing the conflicting demands of power and performance for various applications. Existing approaches either use application-specific profiles or some adhoc heuristics or simpler machine learning models. These techniques often do not unleash the full potential of PSC as they fail to explore and exploit PSC opportunities to a larger extent. Towards that end, we propose the first deep neural network (DNN) based PSC technique, called Forecaster. Forecaster exploits several intuitive observations to cope with the long inference latency of a DNN model and boost customization impact. Forecaster works in two phases. In Phase 1, Forecaster builds a dataset and then, selects and trains a suitable DNN model offline. In Phase 2, Forecaster periodically collects hardware telemetry and uses the trained model to customize hardware resources. We provide a detailed design and implementation of Forecaster and compare its performance against a prior state-of-the-art approach. Our experimental results indicate that on average, Forecaster provides 2.5X more power efficiency gain over the best static configuration setup while sacrificing less than 1.0% of overall performance and less than 3.5% extra system power. Compared to the prior scheme, Forecaster increases the power efficiency gain up to 1.5X while reducing the performance degradation by 44%.

Original languageEnglish (US)
Title of host publicationArchitecture of Computing Systems - 36th International Conference, ARCS 2023, Proceedings
EditorsGeorgios Goumas, Sven Tomforde, Jürgen Brehm, Stefan Wildermann, Thilo Pionteck
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
ISBN (Print)9783031427848
StatePublished - 2023
Event36th International Conference on Architecture of Computing Systems, ARCS 2023 - Athens, Greece
Duration: Jun 13 2023Jun 15 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13949 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference36th International Conference on Architecture of Computing Systems, ARCS 2023


  • Deep neural network
  • FPGA
  • post-silicon customization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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