Auto Hyperparameter Tuning SQLFlow allows the users to specify hyperparameter values via the WITH clause when training models. However, most users under our survey prefer that SQLFlow could automatically estimate these hyperparameters instead. This document is about the automatic hyperparameter estimation. Azure Machine Learning allows you to warm start your hyperparameter tuning run by leveraging knowledge from up to 5 previously completed / cancelled hyperparameter tuning parent runs. You can specify the list of parent runs you want to warm start from using this snippet. Jul 26, 2019  Auto Tune Models - A multi-tenant, multi-data system for automated machine learning (model selection and tuning).

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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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  DOI:10.1145/2847263

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  • Deming Chen,
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