Test and Measurement Product of the Year
Company Name: Ambiq
Product Name: heliaPROFILER™
Supporting Statement:
As edge AI moves from research into real-world deployment, developers face a fundamental challenge: they can determine whether an AI model runs on an embedded device, but not how it performs once deployed, where cycles were spent, which layers dominated power draw, or how one runtime strategy compares to another on identical silicon. Understanding exactly where execution time is spent, which operators consume the most cycles, how different runtimes compare, or how software choices affect energy consumption has traditionally required custom build scripts, manual flashing, fragmented profiling tools, and labor-intensive analysis.
This measurement gap has slowed AI optimization across the embedded industry, making accurate, repeatable benchmarking difficult and often forcing engineering teams to rely on estimation rather than objective hardware data.
heliaPROFILER™ closes this gap.
As the newest open-source addition to Ambiq’s HELIA™ AI ecosystem, heliaPROFILER transforms AI performance analysis into a single automated workflow. With one command (hpx profile), developers can build firmware, flash production hardware, capture cycle-accurate hardware performance counters, and generate comprehensive performance reports. What previously required multiple disconnected tools and extensive manual effort is now completed through a repeatable, production-ready measurement pipeline.
More importantly, heliaPROFILER delivers the depth and accuracy expected of a true test-and-measurement solution—not simply a developer utility.
It provides:
Cycle-accurate, layer-level profiling with cycle counts, instruction counts, cache statistics, and detailed per-layer execution analysis that precisely identifies computational bottlenecks.
Controlled runtime benchmarking, enabling developers to compare TensorFlow Lite Micro, heliaRT™ (interpreter), and heliaAOT™ (ahead-of-time compiler) using identical workloads and hardware for objective, apples-to-apples performance evaluation.
Cross-toolchain measurement, supporting GCC, Arm Compiler for Embedded (ACfE), and Arm Toolchain for Embedded (ATfE), allowing software optimization decisions to be validated with consistent measurement methodology.
Real energy measurement, through optional Joulescope integration, capturing actual power consumption and energy per inference on hardware rather than relying on software estimates or simulation.
Visual performance analysis, exporting layer-by-layer metrics directly into Google’s Model Explorer so developers can immediately visualize execution hotspots and optimization opportunities.
Together, these capabilities elevate heliaPROFILER beyond conventional profiling software into a comprehensive test and measurement platform purpose-built for edge AI development. It delivers objective, hardware-verified data that enables developers to make faster, evidence-based optimization decisions with greater confidence.
Unlike traditional benchmarking approaches that rely on simulation or synthetic workloads, heliaPROFILER measures AI performance directly on production silicon. This is particularly significant for battery-powered edge AI devices, where small differences in execution efficiency translate directly into battery life, thermal performance, and overall product viability. By providing accurate performance and energy insights early in development, heliaPROFILER reduces engineering iteration, shortens development cycles, and helps developers optimize AI applications before products reach production.
The platform also strengthens Ambiq’s broader HELIA AI software ecosystem, complementing heliaCORE™, heliaRT™, and heliaAOT™ to provide developers with a complete workflow spanning AI validation, profiling, optimization, and deployment. Running on Ambiq’s ultra-low-power SoCs—built on the company’s patented Subthreshold Power Optimized Technology (SPOT®) platform and deployed in more than 300 million devices worldwide—heliaPROFILER bridges advanced silicon innovation with software optimization, enabling developers to fully realize the benefits of ultra-low-power edge AI.
Importantly, Ambiq has made heliaPROFILER freely available as an open-source Python package on PyPI (pip install helia-profiler) and GitHub, lowering barriers to adoption while accelerating innovation across the broader edge AI community. By making sophisticated hardware profiling accessible to every developer, heliaPROFILER is helping establish a new standard for measuring, optimizing, and deploying AI at the edge.
Entry ID: 7775
