Microarchitecture Configuration via Machine Learning Proxy
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Solution Overview
Problem
The process of designing a microarchitecture for processors is complex and time-consuming due to the vast number of parameters and settings involved, requiring manual intervention and sequential testing through cycle-accurate simulators, which is inefficient and labor-intensive.
Innovation Solution
A system utilizing a fast approximation simulator implemented by a machine learning model to estimate performance indicators for candidate configurations, reducing the need for full simulations and enabling iterative optimization without manual intervention, using a Monte Carlo Markov Chain approach to propose new parameter combinations and continuously improve accuracy with feedback from full simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cycle-accurate simulators are used to test microarchitecture configurations, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent creates a simplified proxy model (copy) of the complex cycle-accurate simulator. This proxy uses machine learning to approximate the performance indicators without performing full cycle-accurate simulation, thereby reducing time loss while maintaining acceptable measurement precision for design exploration
Solution Approach 2:
The system performs preliminary filtering using the fast proxy model before committing to time-consuming full simulations. By pre-evaluating many candidate configurations with the approximate model, only the most promising ones undergo full cycle-accurate simulation, significantly reducing overall design time
2Ease of operation
If manual intervention is used to select parameters, then ease of operation is maintained, but productivity decreases
Solution Approach 1:
The system enables self-service automation where the microarchitecture design process performs parameter selection and optimization automatically using machine learning models and automated exploration algorithms, eliminating the need for manual parameter tuning while dramatically increasing productivity
Solution Approach 2:
The system implements automated feedback loops where performance measurements from simulations or hardware emulators are automatically fed back to the machine learning models, which then adjust parameter selections iteratively to optimize performance without human intervention
3Reliability
If sequential testing is used through simulators, then reliability is improved, but productivity decreases
Solution Approach 1:
The system dynamically adapts the testing strategy based on confidence levels. For well-explored parameter regions, it uses faster approximate evaluation methods. For uncertain or critical regions, it automatically triggers more reliable but time-consuming full simulations or hardware emulation, optimizing the balance between reliability and productivity
Solution Approach 2:
The design space is segmented into multiple regions or levels of evaluation. Different configurations are tested at different fidelity levels: fast approximate simulation for initial screening, full cycle-accurate simulation for detailed analysis, and hardware emulation for final validation. This segmentation allows parallel exploration of multiple configurations at appropriate validation levels
Data Source
AI summary
Systems and methods for determining a configuration for a microarchitecture are described herein. An example system includes a proposal generator to generate a first candidate configuration of parameters for the microarchitecture, a machine learning model to process the first candidate configuration of parameters to output estimated performance indicators for the microarchitecture, an uncertainty checker to determine whether the estimated performance indicators are reliable, and a performance checker. In response to a determination that the estimated performance indicators are reliable, the performance checker is to determine whether the estimated performance indicators have improved toward a target. Further, if the estimated performance indicators have improved, the performance checker is to store the first candidate configuration of parameters in a memory as a potential solution for a microarchitecture without performing a full simulation on the first candidate configuration of parameters.