Recent progress in building large-scale quantum devices for exploring quantum computing and simulation paradigms has relied upon effective tools for achieving and maintaining good experimental parameters, i.e. Sep 06, 2018  Hyperparameters contain the data that govern the training process itself. Your training application handles three categories of data as it trains your model:. Your input data. And tuning in machine learning is an automated process for doing this. For example, there is no such thing as a 'perfect set' of optimizations for all deployments of an Apache web server. Jul 26, 2019  Overview. Auto Tune Models (ATM) is an AutoML system designed with ease of use in mind. In short, you give ATM a classification problem and a dataset as a CSV file, and ATM.

1. Machine Learning Auto Tuning Kit
2. Guitar Tuning Machines
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4. Machine Learning Based Auto-tuning For Enhanced Opencl Performance Portability

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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)

Machine Learning Auto Tuning Kit

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  DOI:10.1145/2847263

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  • Deming Chen,
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Machine Learning Based Auto-tuning For Enhanced Opencl Performance Portability