How to enable seamless TinyML development and deployment

Unsupervised Federated Learning

Brainchip’s Specialized Akida Architecture for Low Power Edge Inference with HW Spatiotemporal Acceleration and On-chip Learning

Benchmarking AI compiler for the TinyML market

PyNetsPresso and LaunchX: An Integrated Toolchain for Hardware-Aware AI Model Optimization and Benchmarking

Target Classification on the Edge using mmWave Radar: A Novel Algorithm and Its Real-Time Implementation on TI’s IWRL6432

Accelerate Baidu PaddlePaddle Edge AI Software Development with Arm Virtual Hardware

All Analog Compute for Ultra-Low Power Neural Network Processing

Oculi Enables 600x Reduction in Latency-Energy Factor for Visual Edge Applications by Moving Sensors from Imaging to Vision

Suitability of Forward-Forward and PEPITA Learning to MLCommons-Tiny benchmarks