Archives: Presentations

Saving 95% of your edge power with Sparsity to enable tiny ML

Low-Power Computer Vision

tinyML doesn’t need Big Data, it needs Great Data

SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers

Low-Power Embedded Gesture Recognition Using Novel Short-Range Radar Sensors

Low-cost neural network inferencing on the edge with xcore.ai

Using TensorFlow Lite for Microcontrollers for High-Efficiency NN Inference on Ultra-Low Power Processors

Embedded Computer Vision Hardware through the Eyes of AR/VR

Unsupervised collaborative learning technology at the Edge for industrial machine vendors

Once-for-All: Train One Network and Specialize it for Efficient Deployment