Ambiq opens heliaPROFILER: a profiling tool for ultra-low-power edge AI

What is heliaPROFILER

Ambiq has introduced heliaPROFILER, an open-source profiling tool designed to speed up the development and optimization of AI applications on its Apollo microcontrollers. The profiler provides cycle-accurate visibility into AI model execution, allowing developers to balance computational performance against strict power and memory constraints.

The tool automates a workflow that was previously fragmented: instead of jumping between multiple tools and doing manual data analysis, heliaPROFILER integrates model profiling, execution analysis, and report generation into a single flow.

How it works

Developers can collect runtime information directly from production hardware with a single command. Analysis runs at the individual layer level to isolate bottlenecks, and the tool supports memory utilization monitoring and real-time power measurement.

HeliaPROFILER generates visual reports compatible with Google Model Explorer, which simplifies interpreting performance data. It also lets you compare applications running on Ambiq’s two execution engines: heliaRT (runtime) and heliaAOT (ahead-of-time compilation).

The profiler is distributed as an open-source project via PyPI and GitHub, and in this alpha release it already supports Apollo platforms, with additional hardware compatibility planned for version 1.0.

The context: why it matters

As edge AI models grow more complex, the need for detailed visibility into runtime behavior grows with them. Ultra-low-power devices like those Ambiq builds operate under memory and power constraints that leave no room for inefficiency.

Without automated profiling tools, optimizing an embedded model is laborious: change architecture, quantization, or compute distribution, recompile, measure, compare. Every iteration costs time, and it is easy to lose track of which change produced which effect. A profiler that integrates the full flow reduces that noise.

Ambiq places heliaPROFILER inside its broader HELIA ecosystem, which already includes heliaCORE for development, heliaRT for runtime execution, and heliaAOT for ahead-of-time compilation. The profiler is the missing piece that closes the development–optimization–deployment loop in a single suite.

Implications for device design

Open-source, integrated profiling tools lower friction in the development cycle. It was not impossible to optimize models on Apollo before—it just required considerable manual effort or third-party tooling. A native profiler that generates automatic reports lets developers focus on architecture decisions instead of spending time on instrumentation.

That matters especially for small teams or chip vendors who need fast demos. The less time spent on tooling, the more time spent on better models.

Relevance for voice interfaces

At bitbitbla we ship native voice firmware for microcontrollers that manages the AI agent’s voice reliably and keeps a solid connection with smooth audio handling. The firmware maintains low-latency, zero-overhead bidirectional audio streaming, implemented directly on the microcontroller without intermediate layers, and preserves a persistent WebSocket connection with the platform to eliminate open latency and deliver real-time audio.

While tools like Ambiq’s heliaPROFILER aim to optimize AI running locally on the chip itself, at BitBitBla we take a different path to guarantee maximum performance: freeing the microcontroller from that enormous cognitive load.

Our native firmware (designed for ESP32 ecosystems) does not embed the language model; it acts as an ultra-fast terminal. It keeps bidirectional audio streaming and a persistent WebSocket connection with the cloud or private servers, delegating processing to agentic AI on servers. Both approaches—Ambiq’s on-device processing and our ultra-light streaming—share the same goal: extreme efficiency and eliminating latency on hardware with limited resources.

Source: Embedded.com.