Autonomous Processing System Optimization via Machine Learning
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Solution Overview
Problem
Current methods for improving processing systems, such as altering memory addresses or access patterns, are inefficient and require manual trial-and-error, making it difficult to optimize performance metrics like latency, throughput, and power consumption, especially as they vary over time and with different hardware architectures.
Innovation Solution
A system employing machine learning, specifically a combination of genetic methods and neural networks, to identify and apply variations in memory access patterns, instruction flows, and other processing system parameters to autonomously optimize goals like memory utilization, latency, and power usage, without explicit programming.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual trial-and-error methods are used to optimize processing system parameters, then users can attempt to improve performance metrics, but the process becomes inefficient and time-consuming
Solution Approach 1:
The system employs machine learning models that autonomously identify and apply optimal variations to processing system parameters without requiring manual intervention. The learning system continuously monitors performance metrics and self-adjusts parameters such as memory access patterns, instruction flows, and hardware configuration to optimize throughput, latency, and power consumption automatically
Solution Approach 2:
The patent replaces manual mechanical trial-and-error adjustment with automated machine learning algorithms. Instead of users physically configuring and testing different parameter combinations, the system uses neural networks and other ML techniques to predict optimal configurations and apply them programmatically, dramatically accelerating the optimization process
2Reliability
If extensive customization is performed for each hardware architecture, then optimal performance can be achieved, but the complexity and effort required increases significantly
Solution Approach 1:
The machine learning system is designed to be architecture-agnostic and can adapt to different hardware platforms without requiring complete reconfiguration. The learning models generalize across various processor architectures, memory systems, and hardware configurations by learning fundamental performance patterns that apply universally, reducing the need for architecture-specific customization while maintaining optimal performance
Solution Approach 2:
Instead of customizing the entire system for each architecture, the patent dynamically adjusts specific parameters such as memory access patterns, instruction scheduling, and hardware resource allocation based on real-time performance feedback. The system modifies these parameters adaptively to suit different hardware architectures without requiring fundamental system redesign
3Productivity
If users manually optimize processing parameters, then some performance improvement can be achieved, but it becomes difficult to optimize multiple metrics simultaneously
Solution Approach 1:
The system implements continuous feedback loops where machine learning models monitor multiple performance metrics simultaneously (throughput, latency, power consumption) and use this feedback to iteratively refine parameter configurations. The learning system adjusts parameters based on composite performance signals, automatically balancing multiple competing objectives without requiring users to manually tune each metric
Data Source
AI summary
A system and corresponding method improve a processing system. The system comprises a first learning system coupled to a system controller. The first learning system identifies variations for altering processing of a processing system to meet at least one goal. The system controller applies the variations identified to the processing system. The system further comprises a second learning system coupled to the system controller. The second learning system determines respective effects of the variations identified and applied. The first learning system converges on a given variation of the variations based on the respective effects determined. The given variation enables the at least one goal to be met, improving the processing system, such as by increasing throughput, reducing latency, reducing power consumption, reducing temperature, etc.