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Found 17 papers, 1 papers with code
MELTing point: Mobile Evaluation of Language Transformers
Transformers have revolutionized the machine learning landscape, gradually making their way into everyday tasks and equipping our computers with ``sparks of intelligence''.
A collection of the accepted papers for the Human-Centric Representation Learning workshop at AAAI 2024
This non-archival index is not complete, as some accepted papers chose to opt-out of inclusion.
Maestro: Uncovering Low-Rank Structures via Trainable Decomposition
We further apply our technique on DNNs and empirically illustrate that Maestro enables the extraction of lower footprint models that preserve model performance while allowing for graceful accuracy-latency tradeoff for the deployment to devices of different capabilities.
Federated Learning for Inference at Anytime and Anywhere
Federated learning has been predominantly concerned with collaborative training of deep networks from scratch, and especially the many challenges that arise, such as communication cost, robustness to heterogeneous data, and support for diverse device capabilities.
The Future of Consumer Edge-AI Computing
In the last decade, Deep Learning has rapidly infiltrated the consumer end, mainly thanks to hardware acceleration across devices.
Fluid Batching: Exit-Aware Preemptive Serving of Early-Exit Neural Networks on Edge NPUs
With deep neural networks (DNNs) emerging as the backbone in a multitude of computer vision tasks, their adoption in real-world applications broadens continuously.
FedorAS: Federated Architecture Search under system heterogeneity
In this paper we explore the question of whether we can design architectures of different footprints in a cross-device federated setting, where the device landscape, availability and scale are very different.
Smart at what cost? Characterising Mobile Deep Neural Networks in the wild
To this end, we analyse over 16k of the most popular apps in the Google Play Store to characterise their DNN usage and performance across devices of different capabilities, both across tiers and generations.
Adaptive Inference through Early-Exit Networks: Design, Challenges and Directions
DNNs are becoming less and less over-parametrised due to recent advances in efficient model design, through careful hand-crafted or NAS-based methods.
Multi-Exit Semantic Segmentation Networks
At the same time, the heterogeneous capabilities of the target platforms and the diverse constraints of different applications require the design and training of multiple target-specific segmentation models, leading to excessive maintenance costs.
DynO: Dynamic Onloading of Deep Neural Networks from Cloud to Device
Recently, there has been an explosive growth of mobile and embedded applications using convolutional neural networks(CNNs).
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
FjORD alleviates the problem of client system heterogeneity by tailoring the model width to the client's capabilities.
It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation
On-device machine learning is becoming a reality thanks to the availability of powerful hardware and model compression techniques.
SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud
Despite the soaring use of convolutional neural networks (CNNs) in mobile applications, uniformly sustaining high-performance inference on mobile has been elusive due to the excessive computational demands of modern CNNs and the increasing diversity of deployed devices.
HAPI: Hardware-Aware Progressive Inference
Convolutional neural networks (CNNs) have recently become the state-of-the-art in a diversity of AI tasks.
ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning
Our results show that in the absence of retraining our RL-based search is an effective and practical method to compress a production-grade ASR system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices
In recent years, advances in deep learning have resulted in unprecedented leaps in diverse tasks spanning from speech and object recognition to context awareness and health monitoring.
