Archives: Presentations

A VM/Containerized Approach for Scaling TinyML Applications

Privacy-Preserving Inference on the Edge: Mitigating a New Threat Model

Does Form Follow Function? An Empirical Exploration of the Impact of Deep Neural Network Architecture Design on Hardware-Specific Acceleration

Resource Efficient Deep Reinforcement Learning for Acutely Constrained TinyML Devices

TENT: Efficient Quantization of Neural Networks on the tiny Edge with Tapered FixEd PoiNT

Deep Learning for Compute in Memory

Green Accelerated Hoeffding Tree

An Ultra-low Power RNN Classifier for Always-On Voice Wake- Up Detection Robust to Real-World Scenarios

Hypervector Design for Efficient Hyperdimensional Computing on Edge Devices

SWIS – Shared Weight bIt Sparsity for Efficient Neural Network Acceleration