heliaRT™ Runtime for Ultra-Low-Power Edge AI

heliaRT manages and executes AI inference workloads on resource-constrained edge devices, helping developers deploy high-performance edge AI applications with low latency and power consumption.

heliaRT is designed for wearables, healthcare devices, smart home products, industrial IoT systems, voice-enabled devices, and other battery-powered edge AI applications.

heliaRT optimizes runtime execution, memory allocation, and AI inference efficiency, enabling faster model execution and lower power consumption on embedded edge devices.

Yes. heliaRT is built on TensorFlow Lite for Microcontrollers and is optimized for Ambiq’s ultra-low power SoCs.

Runtime optimization improves AI inference speed, reduces memory requirements, lowers latency, and extends battery life for resource-constrained edge AI devices.

heliaRT is specifically optimized for ultra-low power edge AI, enabling efficient deployment on SoCs and embedded systems with limited compute and memory resources.

Yes. heliaRT is designed for ultra-low power AI applications where battery efficiency and real-time AI inference are critical.

Healthcare, wearables, smart home automation, industrial IoT, consumer electronics, and other edge AI applications all benefit from heliaRT’s runtime optimization capabilities.
Industries including healthcare, wearables, smart home automation, industrial IoT, consumer electronics, and edge AI solutions benefit from heliaRT runtime optimization capabilities. 

No. heliaRT is purpose-built for Ambiq’s Apollo family of ultra-low power SoCs, leveraging the hardware architecture to deliver optimizations that general-purpose runtimes can’t match. From memory allocation and runtime execution to low-latency AI inference, every layer of heliaRT is tuned specifically for Ambiq silicon — making Ambiq the best platform for edge AI development.