Summary
Researchers at UCLA have developed a framework that uses bottleneck analysis to enable rapid, explainable hardware/software co-design for domain-specific computing systems. The system constructs a cost model to identify performance bottlenecks and uses that insight to guide efficient design space exploration, producing optimized hardware/software configurations more quickly and transparently.
Background
Designing domain-specific hardware accelerators (for tasks like deep neural network inference or specialized computing workloads) requires exploring a vast space of possible hardware/software configurations. Existing methods often rely on black-box search techniques (e.g. random search, grid search, genetic algorithms, Bayesian optimization) that are computationally expensive, opaque, and slow, especially as the design space grows. Designers and engineers need tools that not only find efficient configurations but also explain why certain design trade-offs exist and where performance bottlenecks lie.
Innovation
This technology introduces an explainability-guided design framework. It builds a bottleneck model (including a bottleneck cost graph) for a workload or function on a given processor/accelerator. The model captures various factors that contribute to execution cost (latency, energy, memory, etc.). Using bottleneck analysis, the system identifies which design parameters (e.g. number of processing elements, memory bandwidth, shared memory sizes) most strongly influence performance. Then, it performs hardware/software codesign and optimization guided by these bottleneck factors, enabling designers to understand trade-offs, prune inefficient configurations, and converge on high-performance solutions more quickly.
Advantages
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Faster convergence in design space exploration due to guided pruning of irrelevant or low-impact parameters.
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Explainability: provides insight into which resources or design choices are limiting performance (rather than treating the system as a black box).
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More efficient use of computational resources during optimization.
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Better optimization of hardware/software trade-offs in latency, energy, memory, etc.
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Improves predictability and reproducibility of optimized designs.
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Especially well suited for domain-specific accelerators or workload-specific system design where certain constraints dominate.
Potential Applications
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Designing accelerators for machine learning workloads (e.g. inference engines, vision models, NLP).
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Systems where latency, power, or area constraints require precise trade-offs (e.g. embedded devices, edge computing).
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Hardware/software co-design flows for custom ASICs or FPGA-based accelerators.
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Cloud providers or infrastructure designers designing domain-specific hardware.
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Research into automated hardware synthesis or performance tuning tools.
Patent / Application
US 2024/0134769 A1 — Systems and Methods for Agile and Explainable Optimization of Efficient Hardware/Software Codesigns Using Bottleneck Analysis
Systems and Methods for Agile and Explainable Optimization of Efficient Hardware/Software Codesigns Using Bottleneck Analysis (US20240134769A1) Google Patents