Ambiq Micro, a technology leader in ultra-low-power semiconductor solutions for edge AI, unveils HeliosRT (Runtime) and HeliosAOT (Ahead-of-Time), two new edge AI runtime solutions optimized for the Ambiq Apollo Systems-on-Chip (SoCs) family. These developer tools are designed to significantly enhance the performance and energy efficiency of AI models for the unique demands of edge computing environments.
Addressing Critical Edge AI Challenges
As AI workloads increasingly migrate to edge devices, developers face growing pressure to deliver high performance within strict power budgets. Traditional AI frameworks often struggle in ultra-low-power scenarios, making it difficult to deploy sophisticated AI models in battery-powered devices, such as wearables, hearables, IoT sensors, and industrial monitors.
Ambiq’s new runtime solutions expand its growing portfolio of developer-centric tools, designed to help engineers unlock the full potential of Apollo SoCs. HeliosRT and HeliosAOT offer flexible, high-performance deployment options for edge AI across a wide range of applications, from digital health and smart homes to industrial automation and beyond.
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HeliosRT: Power-Optimized LiteRT
HeliosRT is a performance-enhanced implementation of LiteRT (formerly TensorFlow Lite for Microcontrollers) that is tailored for energy-constrained environments. Fully compatible with existing TensorFlow workflows, HeliosRT introduces key improvements:
- Custom AI kernels optimized for Apollo510’s vector acceleration hardware
- Improved numeric support for audio and speech processing models
- Up to 3x gains in inference speed and power efficiency over standard LiteRT implementations
HeliosAOT: Compiling LiteRT to Optimized C Code
HeliosAOT introduces a ground-up, ahead-of-time compiler that transforms TensorFlow Lite models directly into embedded C code for edge AI deployment. This innovative approach offers runtime-level, or better, performance with additional benefits:
- 15–50% reduction in memory footprint versus traditional runtime-based deployments
- Granular memory control, enabling per-layer weight distribution across Apollo’s memory hierarchy
- Streamlined deployment, with direct integration of generated C code into embedded applications
- Greater flexibility for resource-constrained systems
“The intersection of developer experience and power efficiency is our north star,” said Carlos Morales, VP of AI at Ambiq. “HeliosRT and HeliosAOT are designed to integrate seamlessly with existing AI development pipelines while delivering the performance and efficiency gains that edge applications demand. We believe this is a major step forward in making sophisticated AI truly ubiquitous.”
SOURCE: GlobeNewswire