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Auto Tuning Machine Learning

07.08.2020
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  1. Feb 21, 2020 Auto Tune Models - A multi-tenant, multi-data system for automated machine learning (model selection and tuning).
  2. Jul 03, 2018  Automated hyperparameter tuning of machine learning models can be accomplished using Bayesian optimization. In contrast to random search, Bayesian optimization chooses the next.
Publication: FPGA '16: Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate ArraysFebruary 2016 Pages 276https://doi.org/10.1145/2847263.2847297

Auto Tuning Machine Learning Software

Jul 26, 2019  Auto Tune Models - A multi-tenant, multi-data system for automated machine learning (model selection and tuning). Auto-Keras is an open source software library for automated machine learning, developed at Texas A&M, that provides functions to automatically search for architecture and hyperparameters of deep. Built, a machine-learning model was created to predict the speedup of a given sequence. Finally, the authors experimentally evaluated their proposed approach using polyhedral optimization.

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Modern High-Level Synthesis (HLS) tools allow C descriptions of computation to be compiled to optimized low-level RTL, but expose a range of manual optimization options, compiler directives and tweaks to the developer. In many instances, this results in a tedious iterative development flow to meet resource, timing and power constraints which defeats the purpose of adopting the high-level abstraction in the first place. In this paper, we show how to use Machine Learning routines to predict the impact of HLS compiler optimization on final FPGA utilization metrics. We compile multiple variations of the high-level C code across a range of compiler optimizations and pragmas to generate a large design space of candidate solutions. On the Machsuite benchmarks, we are able to train a linear regression model to predict resources, latency and frequency metrics with high accuracy (R2 > 0.75). We expect such developer-assistance tools to (1) offer insight to drive manual selection of suitable directive combinations, and (2) automate the process of selecting directives in the complex design space of modern HLS design.

  1. N. Kapre, B. Chandrashekaran, H. Ng, and K. Teo. Driving timing convergence of FPGA designs through Machine Learning and Cloud Computing. In Field-Programmable Custom Computing Machines (FCCM), 2015 IEEE 23rd Annual International Symposium on, pages 119--126, May 2015. Google ScholarDigital Library
  2. N. Kapre, H. Ng, K. Teo, and J. Naude. Intime: A Machine Learning approach for efficient selection of FPGA CAD tool parameters. In Proceedings of the 2015 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, FPGA '15, pages 23--26, New York, NY, USA, 2015. ACM. Google ScholarDigital Library
  1. Machine-Learning driven Auto-Tuning of High-Level Synthesis for FPGAs (Abstract Only)
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  • Published in

    298 pages
    DOI:10.1145/2847263
    • General Chair:
    • Deming Chen,
    • Program Chair:

    Copyright © 2016 Owner/Author

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    Association for Computing Machinery

    New York, NY, United States

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