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Found 4 papers, 2 papers with code
Dynamic ConvNets on Tiny Devices via Nested Sparsity
This work introduces a new training and compression pipeline to build Nested Sparse ConvNets, a class of dynamic Convolutional Neural Networks (ConvNets) suited for inference tasks deployed on resource-constrained devices at the edge of the Internet-of-Things.
Adaptive Test-Time Augmentation for Low-Power CPU
Convolutional Neural Networks (ConvNets) are trained offline using the few available data and may therefore suffer from substantial accuracy loss when ported on the field, where unseen input patterns received under unpredictable external conditions can mislead the model.
TentacleNet: A Pseudo-Ensemble Template for Accurate Binary Convolutional Neural Networks
Binarization is an attractive strategy for implementing lightweight Deep Convolutional Neural Networks (CNNs).
CoopNet: Cooperative Convolutional Neural Network for Low-Power MCUs
Fixed-point quantization and binarization are two reduction methods adopted to deploy Convolutional Neural Networks (CNN) on end-nodes powered by low-power micro-controller units (MCUs).
