Statistical Hardware Design With Multi-model Active Learning
- URL: http://arxiv.org/abs/2303.08054v5
- Date: Sun, 9 Apr 2023 05:09:01 GMT
- Title: Statistical Hardware Design With Multi-model Active Learning
- Authors: Alireza Ghaffari, Masoud Asgharian, Yvon Savaria
- Abstract summary: We propose a model-based active learning approach to solve the problem of designing efficient hardware.
Our proposed method provides hardware models that are sufficiently accurate to perform design space exploration as well as performance prediction simultaneously.
- Score: 1.7596501992526474
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: With the rising complexity of numerous novel applications that serve our
modern society comes the strong need to design efficient computing platforms.
Designing efficient hardware is, however, a complex multi-objective problem
that deals with multiple parameters and their interactions. Given that there
are a large number of parameters and objectives involved in hardware design,
synthesizing all possible combinations is not a feasible method to find the
optimal solution. One promising approach to tackle this problem is statistical
modeling of a desired hardware performance. Here, we propose a model-based
active learning approach to solve this problem. Our proposed method uses
Bayesian models to characterize various aspects of hardware performance. We
also use transfer learning and Gaussian regression bootstrapping techniques in
conjunction with active learning to create more accurate models. Our proposed
statistical modeling method provides hardware models that are sufficiently
accurate to perform design space exploration as well as performance prediction
simultaneously. We use our proposed method to perform design space exploration
and performance prediction for various hardware setups, such as
micro-architecture design and OpenCL kernels for FPGA targets. Our experiments
show that the number of samples required to create performance models
significantly reduces while maintaining the predictive power of our proposed
statistical models. For instance, in our performance prediction setting, the
proposed method needs 65% fewer samples to create the model, and in the design
space exploration setting, our proposed method can find the best parameter
settings by exploring less than 50 samples.
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