Decoding GPU Arithmetic: The Unseen Variances in AI Accelerators

The modern AI landscape is dominated by accelerators that rely heavily on matrix multiply-accumulate units (MMAUs). These are the engines powering NVIDIA Tensor Cores and AMD Matrix Cores, key for speeding up deep neural network computations.

The Hidden Arithmetic

While these units are integral to AI processing, they remain something of a black box. Vendors only provide instruction-level or API-level details, leaving the internal floating-point arithmetic behaviors undocumented. This opacity leads to numerical inconsistencies across different vendors and even across architectural generations. The result? Identical inputs can yield different outputs, throwing off accuracy and potentially leading to instability during training.

Why should this matter? Imagine building a skyscraper with inconsistent measurements. The same applies here. These discrepancies can affect the reliability and effectiveness of AI models, critical in applications where precision is non-negotiable.

Introducing Closed-Loop Feature Probing

Enter closed-loop feature probing (CLFP), a framework aiming to unravel these arithmetic mysteries. By systematically constructing models of MMA operations, it offers insights into the arithmetic behaviors of GPUs spanning from NVIDIA Volta to RTX Blackwell and AMD's CDNA series.

This research isn't just academic. It provides the first bit-accurate arithmetic models, explaining the cross-platform numerical discrepancies and pinpointing accuracy issues. But it goes further, revealing four precision bottleneck designs and one asymmetry affecting numerical outcomes. The findings are available open-source at Microsoft's GitHub repository.

The Stakes for AI Performance

So, what's the real impact? For developers and researchers, understanding these hidden behaviors means better error analysis and improved design guidance for future MMAUs. It also offers practical software workarounds. But the broader question looms: If the AI can hold a wallet, who writes the risk model? The precision of these accelerators directly influences the reliability of AI-driven decisions.

As AI continues to permeate industries, the demand for transparency in hardware behavior will only increase. The intersection is real, and while not every project will succeed, those that address these foundational discrepancies will lead the charge. Show me the inference costs. Then we'll talk about the true value of these developments.