Arm has made available a Dynamic Insights tool that leverages artificial intelligence (AI) models to generate recommendations for optimizing software running on its processors using actual data collected at runtime.

Alex Spinelli, senior vice president for AI and developer platforms at Arm, said the Dynamic Insights tool provides both human developers and AI agents with verified evidence that enables them to optimize application performance using insights surfaced by AI models that have been trained by software engineering experts from Arm. That approach ensures AI guidance is based on how the workload and hardware behave together, rather than relying on source code assumptions alone, he added.

Available as a module with a suite of open source Arm Performix tools, the overall goal is to reduce the level of expertise that previously was required to optimize code at a time when IT infrastructure resources are becoming more expensive, said Spinelli.

In general, the number of workloads that now run on Arm-based platforms continues to exponentially increase. Historically, optimizing the performance of any application required a deep understanding of the underlying processor architecture. However, as large language models (LLMs) understand code better, it’s now possible to surface optimization insights in a way that is much more accessible to the average developer, noted Spinelli.

That’s critical because, as the volume of code being generated in the age of AI continues to increase, it’s no longer feasible for software engineers to manually optimize every line of code. The issue, of course, is that in the absence of that optimization, the cost of running applications that consume infrastructure resources inefficiently only continues to rise.

The Dynamic Insights tool identifies, for example, which functions consume the most execution time, whether workloads are constrained by the CPU, memory, or I/O, how effectively AI accelerators are being used, and whether an issue originates elsewhere in the system.

Mitch Ashley, vice president and practice lead for software lifecycle engineering at the Futurum Group, said tuning code for Arm chips has been specialist work, done by a few engineers who could read what the processor was doing at runtime. Dynamic Insights puts that evidence in front of everyday developers so experts are no longer a bottleneck, he added.

The evidence matters more than the recommendation. A suggestion a developer can’t verify is just faster guessing that regresses under real load. Teams still own proving the change holds before it ships, noted Ashley.

At a time when compute capacity is becoming more limited, the need to optimize code in a way that improves efficiency is becoming more critical with each passing day. There will always be a need for software engineers to achieve that goal, but as AI continues to evolve, the number of instances where applications are inefficiently consuming limited IT infrastructure resources should be sharply reduced. The challenge, as always, is getting the right tools into the hands of application developers to ensure the best possible outcome in the first place.