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Found 7 papers, 2 papers with code
Token and Head Adaptive Transformers for Efficient Natural Language Processing
While pre-trained language models like BERT have achieved impressive results on various natural language processing tasks, deploying them on resource-restricted devices is challenging due to their intensive computational cost and memory footprint.
Seeker: Synergizing Mobile and Energy Harvesting Wearable Sensors for Human Activity Recognition
To address these challenges, we propose \emph{Seeker}, a novel approach to efficiently execute DNN inferences for Human Activity Recognition (HAR) tasks, using both an EH-WSN and a host mobile device.
Exploiting Activation based Gradient Output Sparsity to Accelerate Backpropagation in CNNs
By exploiting sparsity in both the forward and backward passes, speedup improvements range from 1. 68$\times$ to 3. 30$\times$ over the sparsity-agnostic baseline execution.
Transformer-based Machine Learning for Fast SAT Solvers and Logic Synthesis
Our model has a scale-free structure which could process varying size of instances.
STAR: Sparse Transformer-based Action Recognition
The cognitive system for human action and behavior has evolved into a deep learning regime, and especially the advent of Graph Convolution Networks has transformed the field in recent years.
Communication-efficient k-Means for Edge-based Machine Learning
We consider the problem of computing the k-means centers for a large high-dimensional dataset in the context of edge-based machine learning, where data sources offload machine learning computation to nearby edge servers.
Robust Coreset Construction for Distributed Machine Learning
Coreset, which is a summary of the original dataset in the form of a small weighted set in the same sample space, provides a promising approach to enable machine learning over distributed data.
