As modern computing applications continue to grow in complexity, from machine learning to embedded systems, they face increasing constraints in both performance and energy efficiency. A newly defended PhD thesis tackles this challenge head-on by advancing the field of precision tuning, a technique that intelligently adjusts the numerical precision of computations to strike a better balance between speed, power, and accuracy.
The research explores two central challenges in tool design for precision tuning: the limitations of static analysis versus the benefits of profile-guided analysis, and the importance of integrating precision tuning within a hardware-software co-design framework.
While traditional static methods rely solely on mathematical estimates and often err on the side of caution, profile-guided tuning dynamically analyzes real execution data to fine-tune precision levels. This approach significantly improves accuracy and performance, as demonstrated on the PolyBench/C benchmark suite. In fact, profile-guided analysis improved numerical accuracy by an order of magnitude in over 80% of cases—and unlocked dramatic performance boosts, including a 10x speedup in the heat-3d benchmark and a 3x speedup in deriche.
But the innovation doesn’t stop at software. The thesis introduces a co-design strategy that aligns software tuning with hardware capabilities. Applied to an FPGA-based floating-point unit, this co-design reduced program energy consumption by up to 55% and slashed design time by an astonishing 2700x compared to conventional gate-level methods.
Real-world use cases also highlight the impact. In Field-Oriented Control (FOC) for motor systems, profile-guided tuning achieved a 594% speedup while preserving precision. In a bicubic image scaling application, profile-guided tuning delivered a 795% speedup with no accuracy loss—while static tuning actually caused a slowdown.
This research sets the stage for more scalable, automated, and energy-efficient computing systems. By demonstrating the power of adaptive precision tuning combined with hardware-aware design, the thesis charts a path forward for next-generation high-performance applications.
