Archives: Presentations

Speech Recognition on low power devices

TinyML as-a-Service – Bringing ML inference to the deepest IoT Edge

System support for efficient multi-resolution visual computing on low power embedded systems

The Deep (Learning) Transformation of Mobile and Embedded Computing

Pushing the limits of RNN Compression using Kronecker Products

Practical application of tinyML in battery powered anomaly sensors for predictive maintenance of industrial assets

Tutorial on micro-kernel based hardware acceleration

Training Neural Networks for Sensors

Towards Ultra-Low Power Embedded Object Detection

Low power CV meets the real world