Search Results for author:
Found 16 papers, 4 papers with code
How Much Is Hidden in the NAS Benchmarks? Few-Shot Adaptation of a NAS Predictor
Neural architecture search has proven to be a powerful approach to designing and refining neural networks, often boosting their performance and efficiency over manually-designed variations, but comes with computational overhead.
Mitigating Memory Wall Effects in CNN Engines with On-the-Fly Weights Generation
In this work, we investigate the implications in terms of CNN engine design for a class of models that introduce a pre-convolution stage to decompress the weights at run time.
PQA: Exploring the Potential of Product Quantization in DNN Hardware Acceleration
We identify PQ configurations that improve performance-per-area for ResNet20 by up to 3. 1$\times$, even when compared to a highly optimized conventional DNN accelerator, with similar improvements on two additional compact DNNs.
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.
Match to Win: Analysing Sequences Lengths for Efficient Self-supervised Learning in Speech and Audio
Self-supervised learning (SSL) has proven vital in speech and audio-related applications.
ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity
When the available hardware cannot meet the memory and compute requirements to efficiently train high performing machine learning models, a compromise in either the training quality or the model complexity is needed.
Protea: Client Profiling within Federated Systems using Flower
Federated Learning (FL) has emerged as a prospective solution that facilitates the training of a high-performing centralised model without compromising the privacy of users.
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.
Federated Self-supervised Speech Representations: Are We There Yet?
The ubiquity of microphone-enabled devices has lead to large amounts of unlabelled audio data being produced at the edge.
On-device Federated Learning with Flower
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.
unzipFPGA: Enhancing FPGA-based CNN Engines with On-the-Fly Weights Generation
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.
A first look into the carbon footprint of federated learning
Despite impressive results, deep learning-based technologies also raise severe privacy and environmental concerns induced by the training procedure often conducted in data centers.
Degree-Quant: Quantization-Aware Training for Graph Neural Networks
Graph neural networks (GNNs) have demonstrated strong performance on a wide variety of tasks due to their ability to model non-uniform structured data.
Flower: A Friendly Federated Learning Research Framework
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.
Searching for Winograd-aware Quantized Networks
Lightweight architectural designs of Convolutional Neural Networks (CNNs) together with quantization have paved the way for the deployment of demanding computer vision applications on mobile devices.
