1. Tuning Machine Learning
2. Auto Tuning Machine Learning Machine
3. Auto Tuning Machine Learning Software
4. Auto Tuning Machine Learning Kit
5. Auto Tuning Game

Dec 15, 2017 What is autotuning? When building a model, the data scientist can set the value of hyperparameters for the model. Examples of hyperparameters are the number of layers in an artificial neural network, the number of trees in a random forest, etc. The modeler has the power to decide these hyperpara. Jan 21, 2019 Machine learning 30 is an algorithmic paradigm in artificial intelligence and computer science to learn patterns in data without explicitly programming about the characteristic features of those.

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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

Tuning Machine Learning

1. Machine-Learning driven Auto-Tuning of High-Level Synthesis for FPGAs (Abstract Only)

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Auto Tuning Machine Learning Machine

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  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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Auto Tuning Machine Learning Software

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