Archives: Presentations

Improving the Energy Efficiency and Robustness of tinyML Computer Vision using Log-Gradient Input Images

Toward Compact Deep Neural Networks via Energy-Aware Pruning

Delta Keyword Transformer: Bringing Transformers to the Edge through Dynamically Pruned Multi-Head Self-Attention

tinyMAN: Lightweight Energy Manager using Reinforcement Learning for Energy Harvesting Wearable IoT Devices

PocketNN: Integer-only Training and Inference of Neural Networks via Direct Feedback Alignment and Pocket Activations in Pure C++

LDP: Learnable Dynamic Precision for Efficient Deep Neural Network Training and Inference

IMU Preintegrated Features for Efficient Deep Inertial Odometry

Distributed On-Sensor Compute System for AR/VR Devices: A Semi-Analytical Simulation Framework for Power Estimation

How to Manage Tiny Machine Learning at Scale – An Industrial Perspective

Millimeter-Scale Ultra-Low-Power Imaging System for Intelligent Edge Monitoring