Profile-Guided Integrated Circuit Optimization for Unequal Branch Resources
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Solution Overview
Problem
Programmable integrated circuits often allocate equal resources to branches that are only activated a small percentage of the time, leading to suboptimal performance and bulkier hardware architectures.
Innovation Solution
Insert non-intrusive profiling blocks into the hardware description to gather profiling data, which is used to identify and optimize hardware resources based on actual usage patterns, iteratively refining the design to better suit the application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If equal resources are allocated to all branches in the design phase, then all branches can function correctly, but the hardware architecture becomes bulkier and performance deteriorates
Solution Approach 1:
The patent applies local quality by allocating different amounts of hardware resources to different branches based on their actual execution frequencies. Profiling data reveals which branches are taken more often, and the compiler optimizes the design by providing more resources to frequently-taken branches while reducing resources to rarely-taken branches, creating non-uniform resource distribution matched to actual usage patterns
Solution Approach 2:
The patent changes the resource allocation parameters from equal distribution to unequal distribution based on profiling data. The compiler uses execution frequency metrics to adjust hardware resource parameters (such as buffer sizes, register allocations, or logic element counts) for each branch, transforming the static equal-allocation design into a dynamic, usage-aware configuration
2Reliability
If equal resources are allocated to all branches in the design phase, then all branches can function correctly, but the hardware architecture becomes bulkier
Solution Approach 1:
The patent applies local quality by allocating different amounts of hardware resources to different branches based on their actual execution frequencies. Profiling data reveals which branches are taken more often, and the compiler optimizes the design by providing more resources to frequently-taken branches while reducing resources to rarely-taken branches, creating non-uniform resource distribution matched to actual usage patterns
Solution Approach 2:
The patent changes the resource allocation parameters from equal distribution to unequal distribution based on profiling data. The compiler uses execution frequency metrics to adjust hardware resource parameters (such as buffer sizes, register allocations, or logic element counts) for each branch, transforming the static equal-allocation design into a dynamic, usage-aware configuration
3Reliability
If comprehensive optimization is performed in the design phase, then all possible scenarios are covered, but the design becomes more complex and less efficient
Solution Approach 1:
The patent applies preliminary action by performing profiling and optimization iterations before finalizing the hardware design. The compiler executes multiple optimization passes with increasing aggressiveness, using profiling data from simulated or actual runtime behavior to guide resource allocation decisions, thereby resolving design complexity issues before manufacturing
Solution Approach 2:
The patent applies feedback by using profiling data collected from branch execution frequencies to guide subsequent optimization decisions. The compiler analyzes the profiling information and adjusts resource allocation accordingly, creating a feedback loop where runtime behavior information informs design optimization, allowing the system to adapt to actual usage patterns rather than assuming worst-case scenarios
Data Source
AI summary
Methods and apparatus for performing profile-guided optimization of integrated circuit hardware are provided. Circuit design tools may receive a source code and compile the source code to generate a hardware description. The hardware description may include profiling blocks configured to measure useful information required for optimization. The hardware description may then be simulated to gather profiling data. The circuit design tools may then analyze the gathered profiling data to identify additional opportunities for hardware optimization. The source code may then be modified based on the analysis of the profiling data to produce a smaller and faster hardware that is better suited to the application.