ArmSense AI — Edge TinyML Controller

Ins## Inspiration

We were inspired to bring fast, private, and energy-efficient AI control directly to edge microcontrollers. Many current systems rely heavily on cloud APIs, creating latency issues and privacy risks. ArmSense AI solves this by keeping all intelligence on-device.

What it does

ArmSense AI is an ultra-lightweight TinyML gesture and voice command controller optimized for Arm architectures. It processes control inputs locally with sub-10ms execution latency while keeping power consumption extremely low.

How we built it

We built the system using Python and TensorFlow Lite. We applied INT8 quantization specifically targeting Arm Cortex-M and Arm64/ARMv8/ARMv9 architectures to maintain a minimal memory footprint without sacrificing operational accuracy.

Challenges we faced

Optimizing the model to run efficiently on memory-constrained hardware while maintaining fast recognition speed was a key challenge. We overcame this through INT8 quantization and memory allocation tweaks.

What we learned & Impact

We learned how to squeeze high-performance inference out of lightweight embedded hardware. ArmSense AI provides an open-source template and quantized model asset for developers building embedded AI and HMI applications on the Arm ecosystem. piration

What it does

How we built it

Challenges we ran into

Accomplishments that we're proud of

What we learned

What's next for ArmSense AI — Edge TinyML Controller

Built With

• edge
• python
• tensorflow
• tinyml