AI Hardware Software Co-Design for Accelerator Optimization
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Solution Overview
Problem
Modern AI workloads process large data sets inefficiently on standard or generically-configured hardware processors, which often lack optimal configurations for specific functions or workloads, leading to suboptimal performance.
Innovation Solution
The system employs a hardware and software co-design approach that iteratively determines optimized configurations by co-designing hardware and software elements, using machine learning regression processes with active learning to simulate and evaluate various device configurations, thereby maximizing model accuracy and minimizing hardware costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If standard or generically-configured hardware processors are used, then hardware simplicity and ease of deployment are maintained, but processing efficiency and accuracy for AI workloads deteriorate
Solution Approach 1:
The patent applies parameter changes by systematically varying hardware parameters (architecture type, memory bandwidth, precision format) and software parameters (model architecture, optimization level) to identify optimal configurations. This transforms the hardware selection from a static generic choice to a dynamic parameter optimization process that adapts to specific AI workload requirements.
Solution Approach 2:
The system introduces dynamics through iterative evaluation and active learning mechanisms that continuously refine hardware/software configuration recommendations. The methodology evolves from initial generic configurations to optimized dynamic configurations through simulated workloads and performance modeling, enabling adaptive hardware selection rather than static deployment.
2Reliability
If manual hardware configuration optimization is performed, then processing accuracy and performance are improved, but time consumption and development cost increase
Solution Approach 1:
The patent employs copying by creating virtual simulations of hardware architectures and workloads. Instead of physically testing every configuration combination, the system copies the essential characteristics of hardware and software components into a virtual evaluation environment, allowing rapid iteration and optimization without physical prototyping or manual trial-and-error configuration.
Solution Approach 2:
The system performs preliminary action through pre-computed performance models and active learning frameworks that predict optimal configurations before actual deployment. By preparing configuration recommendations in advance through simulated evaluations, the system eliminates time-consuming manual optimization during production and focuses only on verifying top candidates.
3Productivity
If hardware is optimized for specific functions, then processing performance is improved, but hardware versatility and adaptability to different workloads decrease
Solution Approach 1:
The patent applies universality by evaluating and recommending hardware configurations that can serve multiple functions. The methodology identifies hardware architectures capable of supporting diverse AI workloads through flexible configuration options, enabling a single hardware platform to adapt to different model types, precision requirements, and performance constraints without requiring specialized dedicated hardware for each function.
Data Source
AI summary
Systems and methods are provided for iteratively co-designing hardware and software elements of a device configuration to optimize it. This process can predict how software parameters and hardware parameters will perform in the device configuration using a machine learning process that simulates how the device configuration will perform. The corresponding input (e.g., software/hardware parameters) and output (e.g., model accuracy evaluation value for software parameters and hardware cost estimation value for hardware parameters) determined from the machine learning process can be used for various purposes, including used to train a machine learning (ML) model to select the optimized device configuration in view of the various constraints.