Archives: Presentations

Implementation Considerations for Machine Learning at the Edge of the Cloud

The Akida Neural Processor: Low Power CNN Inference and Learning at the Edge

Towards Software-Defined Imaging: Adaptive Video Subsampling for Energy-Efficient Object Tracking

Pushing the Limits of Ultra-low Power Computer Vision for tinyML Applications

AI/ML SoC for Ultra-Low-Power Mobile and IoT devices

A Review of Compression Methods for Deep Convolutional Neural Networks

How to train and deploy tiny ML models for three common sensor types

Benchmarking and Improving NN Execution on Digital Signal Processor vs. Custom Accelerator for Hearing Instruments

Analog ML Is Relevant—Because Most Sensor Content Isn’t

A weight-averaging approach to speeding up model training on resource-constrained devices