Predicting compute job resources and compute time for computation jobs of design of semiconductor devices using machine learning models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current EDA tools fail to accurately determine the appropriate computational resources and licenses needed for semiconductor design tasks, leading to inefficient resource utilization and prolonged job completion times, which slows down the design process.
Innovation Solution
Implementing a machine learning model to predict computational hardware resources and time required for semiconductor design verification by using historical data and machine learning techniques such as deep learning, supervised learning, and reinforcement learning to optimize resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional EDA tools are used to determine computational resources, then the process is simple and deterministic, but resource utilization is inefficient and job completion times are prolonged
Solution Approach 1:
The patent replaces traditional deterministic resource determination methods with machine learning models that use historical data and computational algorithms to predict resource requirements. This substitution enables more efficient resource allocation by leveraging patterns from past computations rather than relying on fixed rules, thereby improving productivity while managing system complexity through automated intelligent decision-making.
Solution Approach 2:
The system employs machine learning models that automatically learn from historical computation data and make independent predictions about resource requirements. This self-service capability allows the system to optimize resource allocation without continuous human intervention, improving resource utilization efficiency while the automated learning process manages the complexity internally.
2Measurement precision
If more computational resources are allocated to ensure accurate prediction, then prediction accuracy improves, but resource consumption increases
Solution Approach 1:
The patent dynamically adjusts the complexity and parameters of machine learning models based on the specific computation job requirements and historical data patterns. This allows the system to achieve high prediction accuracy for complex jobs while using simpler, less resource-intensive models for straightforward tasks, thereby optimizing the trade-off between prediction accuracy and computational resource consumption.
Solution Approach 2:
The system applies machine learning predictions selectively and adjusts resource allocation based on the predicted needs rather than uniformly applying high computational resources to all tasks. This partial action approach ensures accurate predictions are made where needed while avoiding excessive resource consumption on tasks that require less precision, improving overall efficiency.
3Loss of time
If resource allocation is optimized using machine learning, then job queue times are reduced, but the system complexity and initial setup requirements increase
Solution Approach 1:
The patent implements machine learning models that are pre-trained on historical computation data before actual job processing begins. This preliminary action allows the system to make accurate resource predictions without requiring complex real-time analysis during job execution, thereby reducing job queue times while the upfront training process manages the complexity of system setup.
Solution Approach 2:
The system uses feedback from historical computation results to continuously improve machine learning models and refine resource allocation decisions. This feedback mechanism enables the system to learn from past performance and optimize future predictions, reducing job queue times over time while the iterative improvement process manages complexity through gradual learning rather than requiring perfectly predetermined rules.
Data Source
AI summary
A job manager may receive a request for a design of a semiconductor device to be verified by a set of computing devices, wherein the request comprises design parameters regarding the design of the semiconductor device. The job manager may provide the design parameters as inputs to a machine learning model trained to predict amounts of computational resources and amounts of compute time for verifying designs of semiconductor devices. The job manager may obtain, as an output from the machine learning model, a predicted amount of computational resources and a predicted amount of compute time for verifying the design of the semiconductor device. The job manager may determine an availability of resources, of the set of computing devices, for verifying the design of the semiconductor device. The job manager may cause the design of the semiconductor device to be verified by one or more computing devices.