Ambiq Launches heliaAOT 1.0 AI Compiler, Cutting Inference Latency by Up to 76% on Apollo510
The ahead-of-time compiler reduces energy consumption by up to 72% and firmware size by up to 56% compared with LiteRT Micro in benchmark tests. Designed for low-power edge AI devices, heliaAOT 1.0 supports 65 operators and deployment across 15 Apollo SoCs, targeting health monitoring, vision and voice applications
Ambiq® Micro, Inc. (“Ambiq”), a technology leader in ultra-low-power semiconductor solutions for edge AI, introduced heliaAOT 1.0, its ahead-of-time AI compiler for the HELIA™ AI ecosystem. This development further highlights Ambiq’s focus on building an integrated silicon and software platform that enables Edge AI compute on low-power, memory-constrained devices.
heliaAOT converts trained AI models into optimized C code, reducing processing time, energy, and memory required for on-device inference. In benchmark tests on Apollo510, heliaAOT delivered up to 76% lower inference latency and up to 72% lower energy per inference than LiteRT Micro¹. heliaAOT has already been deployed in mass-produced products, and Ambiq is in discussions with some of its larger customers about deploying it in their current products.
“Customers want to bring more intelligence to their devices without exceeding their power and memory budgets,” said Dr. Adam Page, Head of Artificial Intelligence at Ambiq. “heliaAOT 1.0 helps them get more from Apollo silicon, deploy a broader range of models, and reuse their development work across products.”
More AI Capability Across Apollo Families
heliaAOT 1.0 helps developers run more models, use less memory, and reuse their work across Apollo products:
- Broader model support: Run supported FP16 and FP32 models without integer quantization, with approximately 4× faster FP32 inference than LiteRT Micro FP32 in benchmark testing². Support for 65 operators plus custom operators accommodates more model architectures, enabling more AI use cases.
- Smaller footprint: Compared with LiteRT Micro, median firmware images are 29% to 56% smaller, freeing up storage and RAM for more applications³.
- Always-on intelligence: Streaming LSTM, GRU, and SVDF support enables continuous voice, biosignal, and sensor processing for greater device responsiveness.
- Easier deployment: Retarget supported models across 15 Apollo SoCs with a one-line compiler configuration change, saving development time.
In Apollo510 benchmarks against LiteRT Micro, heliaAOT reduced inference latency, energy consumption, and firmware image size across all tested workload groups.
Ambiq tests every release on Apollo silicon. With heliaPROFILER™, developers can measure performance on their own models and boards to assess the benefits for their applications.
Advancing the Apollo AI Ecosystem
Ambiq delivered 17 heliaAOT updates over the past 12 months, expanding operator support from 35 to 65 and Apollo target support from 8 to 15. Recognized as the Best Tools category at Embedded World 2026, heliaAOT strengthens Ambiq’s combined silicon and software offering and helps customers carry their AI development work across products.
