Search Results for author: Nicholas D. Lane

Found 52 papers, 14 papers with code

Multi-DNN Accelerators for Next-Generation AI Systems

no code implementations19 May 2022 Stylianos I. Venieris, Christos-Savvas Bouganis, Nicholas D. Lane

As the use of AI-powered applications widens across multiple domains, so do increase the computational demands.

Secure Aggregation for Federated Learning in Flower

no code implementations12 May 2022 Kwing Hei Li, Pedro Porto Buarque de Gusmão, Daniel J. Beutel, Nicholas D. Lane

Federated Learning (FL) allows parties to learn a shared prediction model by delegating the training computation to clients and aggregating all the separately trained models on the server.

Federated Learning

Differentiable Network Pruning for Microcontrollers

no code implementations15 Oct 2021 Edgar Liberis, Nicholas D. Lane

Embedded and personal IoT devices are powered by microcontroller units (MCUs), whose extreme resource scarcity is a major obstacle for applications relying on on-device deep learning inference.

Model Compression Network Pruning

Smart at what cost? Characterising Mobile Deep Neural Networks in the wild

no code implementations28 Sep 2021 Mario Almeida, Stefanos Laskaridis, Abhinav Mehrotra, Lukasz Dudziak, Ilias Leontiadis, Nicholas D. Lane

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.

AgreementLearning: An End-to-End Framework for Learning with Multiple Annotators without Groundtruth

no code implementations8 Sep 2021 Chongyang Wang, Yuan Gao, Chenyou Fan, Junjie Hu, Tin Lun Lam, Nicholas D. Lane, Nadia Bianchi-Berthouze

The framework has two streams, with one stream fitting with the multiple annotators and the other stream learning agreement information between the annotators.

Temporal Kernel Consistency for Blind Video Super-Resolution

no code implementations18 Aug 2021 Lichuan Xiang, Royson Lee, Mohamed S. Abdelfattah, Nicholas D. Lane, Hongkai Wen

Deep learning-based blind super-resolution (SR) methods have recently achieved unprecedented performance in upscaling frames with unknown degradation.

Video Super-Resolution

Zero-Cost Proxies Meet Differentiable Architecture Search

no code implementations12 Jun 2021 Lichuan Xiang, Łukasz Dudziak, Mohamed S. Abdelfattah, Thomas Chau, Nicholas D. Lane, Hongkai Wen

Differentiable neural architecture search (NAS) has attracted significant attention in recent years due to its ability to quickly discover promising architectures of deep neural networks even in very large search spaces.

Neural Architecture Search

Adaptive Inference through Early-Exit Networks: Design, Challenges and Directions

no code implementations9 Jun 2021 Stefanos Laskaridis, Alexandros Kouris, Nicholas D. Lane

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

no code implementations7 Jun 2021 Alexandros Kouris, Stylianos I. Venieris, Stefanos Laskaridis, Nicholas D. Lane

Semantic segmentation arises as the backbone of many vision systems, spanning from self-driving cars and robot navigation to augmented reality and teleconferencing.

Robot Navigation Self-Driving Cars +1

Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions

no code implementations7 Jun 2021 Royson Lee, Stylianos I. Venieris, Nicholas D. Lane

In recent years, advances in the field of deep learning on tasks such as super-resolution and image enhancement have led to unprecedented performance in generating high-quality images from low-quality ones, a process we refer to as neural enhancement.

Image Enhancement Super-Resolution

DynO: Dynamic Onloading of Deep Neural Networks from Cloud to Device

no code implementations20 Apr 2021 Mario Almeida, Stefanos Laskaridis, Stylianos I. Venieris, Ilias Leontiadis, Nicholas D. Lane

Recently, there has been an explosive growth of mobile and embedded applications using convolutional neural networks(CNNs).

On-device Federated Learning with Flower

no code implementations7 Apr 2021 Akhil Mathur, Daniel J. Beutel, Pedro Porto Buarque de Gusmão, Javier Fernandez-Marques, Taner Topal, Xinchi Qiu, Titouan Parcollet, Yan Gao, Nicholas D. Lane

Federated Learning (FL) allows edge devices to collaboratively learn a shared prediction model while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store data in the cloud.

Federated Learning

Do We Need Anisotropic Graph Neural Networks?

2 code implementations3 Apr 2021 Shyam A. Tailor, Felix L. Opolka, Pietro Liò, Nicholas D. Lane

We demonstrate that EGC outperforms existing approaches across 6 large and diverse benchmark datasets, and conclude by discussing questions that our work raise for the community going forward.

unzipFPGA: Enhancing FPGA-based CNN Engines with On-the-Fly Weights Generation

no code implementations9 Mar 2021 Stylianos I. Venieris, Javier Fernandez-Marques, Nicholas D. Lane

Single computation engines have become a popular design choice for FPGA-based convolutional neural networks (CNNs) enabling the deployment of diverse models without fabric reconfiguration.

It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation

no code implementations2 Feb 2021 Ilias Leontiadis, Stefanos Laskaridis, Stylianos I. Venieris, Nicholas D. Lane

On-device machine learning is becoming a reality thanks to the availability of powerful hardware and model compression techniques.

Model Compression

Zero-Cost Proxies for Lightweight NAS

2 code implementations ICLR 2021 Mohamed S. Abdelfattah, Abhinav Mehrotra, Łukasz Dudziak, Nicholas D. Lane

For example, Spearman's rank correlation coefficient between final validation accuracy and our best zero-cost proxy on NAS-Bench-201 is 0. 82, compared to 0. 61 for EcoNAS (a recently proposed reduced-training proxy).

Neural Architecture Search

Leveraging Activity Recognition to Enable Protective Behavior Detection in Continuous Data

1 code implementation3 Nov 2020 Chongyang Wang, Yuan Gao, Akhil Mathur, Amanda C. De C. Williams, Nicholas D. Lane, Nadia Bianchi-Berthouze

Protective behavior exhibited by people with chronic pain (CP) during physical activities is the key to understanding their physical and emotional states.

Activity Recognition

$μ$NAS: Constrained Neural Architecture Search for Microcontrollers

1 code implementation27 Oct 2020 Edgar Liberis, Łukasz Dudziak, Nicholas D. Lane

IoT devices are powered by microcontroller units (MCUs) which are extremely resource-scarce: a typical MCU may have an underpowered processor and around 64 KB of memory and persistent storage, which is orders of magnitude fewer computational resources than is typically required for deep learning.

Image Classification Neural Architecture Search

Neural Enhancement in Content Delivery Systems: The State-of-the-Art and Future Directions

no code implementations12 Oct 2020 Royson Lee, Stylianos I. Venieris, Nicholas D. Lane

In recent years, advances in the field of deep learning on tasks such as super-resolution and image enhancement have led to unprecedented performance in generating high-quality images from low-quality ones, a process we refer to as neural enhancement.

Image Enhancement Super-Resolution

Libri-Adapt: A New Speech Dataset for Unsupervised Domain Adaptation

no code implementations6 Sep 2020 Akhil Mathur, Fahim Kawsar, Nadia Berthouze, Nicholas D. Lane

This paper introduces a new dataset, Libri-Adapt, to support unsupervised domain adaptation research on speech recognition models.

Speech Recognition Unsupervised Domain Adaptation

SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud

no code implementations14 Aug 2020 Stefanos Laskaridis, Stylianos I. Venieris, Mario Almeida, Ilias Leontiadis, Nicholas D. Lane

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.

Degree-Quant: Quantization-Aware Training for Graph Neural Networks

no code implementations ICLR 2021 Shyam A. Tailor, Javier Fernandez-Marques, Nicholas D. Lane

Graph neural networks (GNNs) have demonstrated strong performance on a wide variety of tasks due to their ability to model non-uniform structured data.

Graph Classification Graph Regression +2

Bunched LPCNet : Vocoder for Low-cost Neural Text-To-Speech Systems

no code implementations11 Aug 2020 Ravichander Vipperla, Sangjun Park, Kihyun Choo, Samin Ishtiaq, Kyoungbo Min, Sourav Bhattacharya, Abhinav Mehrotra, Alberto Gil C. P. Ramos, Nicholas D. Lane

LPCNet is an efficient vocoder that combines linear prediction and deep neural network modules to keep the computational complexity low.

HAPI: Hardware-Aware Progressive Inference

no code implementations10 Aug 2020 Stefanos Laskaridis, Stylianos I. Venieris, Hyeji Kim, Nicholas D. Lane

Convolutional neural networks (CNNs) have recently become the state-of-the-art in a diversity of AI tasks.

Flower: A Friendly Federated Learning Research Framework

1 code implementation28 Jul 2020 Daniel J. Beutel, Taner Topal, Akhil Mathur, Xinchi Qiu, Javier Fernandez-Marques, Yan Gao, Lorenzo Sani, Kwing Hei Li, Titouan Parcollet, Pedro Porto Buarque de Gusmão, Nicholas D. Lane

Federated Learning (FL) has emerged as a promising technique for edge devices to collaboratively learn a shared prediction model, while keeping their training data on the device, thereby decoupling the ability to do machine learning from the need to store the data in the cloud.

Federated Learning

BRP-NAS: Prediction-based NAS using GCNs

2 code implementations NeurIPS 2020 Łukasz Dudziak, Thomas Chau, Mohamed S. Abdelfattah, Royson Lee, Hyeji Kim, Nicholas D. Lane

What is more, we investigate prediction quality on different metrics and show that sample efficiency of the predictor-based NAS can be improved by considering binary relations of models and an iterative data selection strategy.

Neural Architecture Search

Journey Towards Tiny Perceptual Super-Resolution

2 code implementations ECCV 2020 Royson Lee, Łukasz Dudziak, Mohamed Abdelfattah, Stylianos I. Venieris, Hyeji Kim, Hongkai Wen, Nicholas D. Lane

Recent works in single-image perceptual super-resolution (SR) have demonstrated unprecedented performance in generating realistic textures by means of deep convolutional networks.

Neural Architecture Search Super-Resolution

IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition

no code implementations29 May 2020 Hyeokhyen Kwon, Catherine Tong, Harish Haresamudram, Yan Gao, Gregory D. Abowd, Nicholas D. Lane, Thomas Ploetz

The lack of large-scale, labeled data sets impedes progress in developing robust and generalized predictive models for on-body sensor-based human activity recognition (HAR).

Activity Recognition

Mic2Mic: Using Cycle-Consistent Generative Adversarial Networks to Overcome Microphone Variability in Speech Systems

no code implementations27 Mar 2020 Akhil Mathur, Anton Isopoussu, Fahim Kawsar, Nadia Berthouze, Nicholas D. Lane

A major challenge in building systems that combine audio models with commodity microphones is to guarantee their accuracy and robustness in the real-world.

Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator

no code implementations11 Feb 2020 Mohamed S. Abdelfattah, Łukasz Dudziak, Thomas Chau, Royson Lee, Hyeji Kim, Nicholas D. Lane

We automate HW-CNN codesign using NAS by including parameters from both the CNN model and the HW accelerator, and we jointly search for the best model-accelerator pair that boosts accuracy and efficiency.

General Classification Image Classification +2

The Final Frontier: Deep Learning in Space

no code implementations27 Jan 2020 Vivek Kothari, Edgar Liberis, Nicholas D. Lane

Machine learning, particularly deep learning, is being increasing utilised in space applications, mirroring the groundbreaking success in many earthbound problems.

Are Accelerometers for Activity Recognition a Dead-end?

no code implementations22 Jan 2020 Catherine Tong, Shyam A. Tailor, Nicholas D. Lane

Overall, our work highlights the need to move away from accelerometers and calls for further exploration of using imagers for activity recognition.

Activity Recognition Feature Engineering

EMOPAIN Challenge 2020: Multimodal Pain Evaluation from Facial and Bodily Expressions

no code implementations21 Jan 2020 Joy O. Egede, Siyang Song, Temitayo A. Olugbade, Chongyang Wang, Amanda Williams, Hongy-ing Meng, Min Aung, Nicholas D. Lane, Michel Valstar, Nadia Bianchi-Berthouze

The EmoPain 2020 Challenge is the first international competition aimed at creating a uniform platform for the comparison of machine learning and multimedia processing methods of automatic chronic pain assessment from human expressive behaviour, and also the identification of pain-related behaviours.

Emotion Recognition

Neural networks on microcontrollers: saving memory at inference via operator reordering

1 code implementation2 Oct 2019 Edgar Liberis, Nicholas D. Lane

Designing deep learning models for highly-constrained hardware would allow imbuing many edge devices with intelligence.

Multi-Step Decentralized Domain Adaptation

no code implementations25 Sep 2019 Akhil Mathur, Shaoduo Gan, Anton Isopoussu, Fahim Kawsar, Nadia Berthouze, Nicholas D. Lane

Despite the recent breakthroughs in unsupervised domain adaptation (uDA), no prior work has studied the challenges of applying these methods in practical machine learning scenarios.

Unsupervised Domain Adaptation

MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors

no code implementations21 Aug 2019 Royson Lee, Stylianos I. Venieris, Łukasz Dudziak, Sourav Bhattacharya, Nicholas D. Lane

In recent years, convolutional networks have demonstrated unprecedented performance in the image restoration task of super-resolution (SR).

Image Restoration Model Compression +1

ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning

no code implementations8 Jul 2019 Łukasz Dudziak, Mohamed S. Abdelfattah, Ravichander Vipperla, Stefanos Laskaridis, Nicholas D. Lane

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 AutoML +3

EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices

no code implementations17 May 2019 Mario Almeida, Stefanos Laskaridis, Ilias Leontiadis, Stylianos I. Venieris, Nicholas D. Lane

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.

Object Recognition

An Empirical study of Binary Neural Networks' Optimisation

1 code implementation ICLR 2019 Milad Alizadeh, Javier Fernández-Marqués, Nicholas D. Lane, Yarin Gal

In this work, we empirically identify and study the effectiveness of the various ad-hoc techniques commonly used in the literature, providing best-practices for efficient training of binary models.

Learning Bodily and Temporal Attention in Protective Movement Behavior Detection

1 code implementation24 Apr 2019 Chongyang Wang, Min Peng, Temitayo A. Olugbade, Nicholas D. Lane, Amanda C. De C. Williams, Nadia Bianchi-Berthouze

For people with chronic pain, the assessment of protective behavior during physical functioning is essential to understand their subjective pain-related experiences (e. g., fear and anxiety toward pain and injury) and how they deal with such experiences (avoidance or reliance on specific body joints), with the ultimate goal of guiding intervention.

Chronic-Pain Protective Behavior Detection with Deep Learning

1 code implementation24 Feb 2019 Chongyang Wang, Temitayo A. Olugbade, Akhil Mathur, Amanda C. De C. Williams, Nicholas D. Lane, Nadia Bianchi-Berthouze

In chronic pain rehabilitation, physiotherapists adapt physical activity to patients' performance based on their expression of protective behavior, gradually exposing them to feared but harmless and essential everyday activities.

Privacy-Preserving Deep Inference for Rich User Data on The Cloud

1 code implementation4 Oct 2017 Seyed Ali Osia, Ali Shahin Shamsabadi, Ali Taheri, Kleomenis Katevas, Hamid R. Rabiee, Nicholas D. Lane, Hamed Haddadi

Our evaluations show that by using certain kind of fine-tuning and embedding techniques and at a small processing costs, we can greatly reduce the level of information available to unintended tasks applied to the data feature on the cloud, and hence achieving the desired tradeoff between privacy and performance.

A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics

1 code implementation8 Mar 2017 Seyed Ali Osia, Ali Shahin Shamsabadi, Sina Sajadmanesh, Ali Taheri, Kleomenis Katevas, Hamid R. Rabiee, Nicholas D. Lane, Hamed Haddadi

To this end, instead of performing the whole operation on the cloud, we let an IoT device to run the initial layers of the neural network, and then send the output to the cloud to feed the remaining layers and produce the final result.

X-CNN: Cross-modal Convolutional Neural Networks for Sparse Datasets

no code implementations1 Oct 2016 Petar Veličković, Duo Wang, Nicholas D. Lane, Pietro Liò

In this paper we propose cross-modal convolutional neural networks (X-CNNs), a novel biologically inspired type of CNN architectures, treating gradient descent-specialised CNNs as individual units of processing in a larger-scale network topology, while allowing for unconstrained information flow and/or weight sharing between analogous hidden layers of the network---thus generalising the already well-established concept of neural network ensembles (where information typically may flow only between the output layers of the individual networks).

Data Augmentation

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