Search Results for author: Christos-Savvas Bouganis

Found 19 papers, 5 papers with code

Multi-Precision Policy Enforced Training (MuPPET) : A Precision-Switching Strategy for Quantised Fixed-Point Training of CNNs

no code implementations ICML 2020 Aditya Rajagopal, Diederik Vink, Stylianos Venieris, Christos-Savvas Bouganis

Large-scale convolutional neural networks (CNNs) suffer from very long training times, spanning from hours to weeks, limiting the productivity and experimentation of deep learning practitioners.

On the Usefulness of Deep Ensemble Diversity for Out-of-Distribution Detection

no code implementations15 Jul 2022 Guoxuan Xia, Christos-Savvas Bouganis

As such we show that practically, even better OOD detection performance can be achieved for Deep Ensembles by averaging task-specific detection scores such as Energy over the ensemble.

OOD Detection Out-of-Distribution Detection

Augmenting Softmax Information for Selective Classification with Out-of-Distribution Data

1 code implementation15 Jul 2022 Guoxuan Xia, Christos-Savvas Bouganis

However, the performance of detection methods is generally evaluated on the task in isolation, rather than also considering potential downstream tasks in tandem.

OOD Detection

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.

Low-Cost On-device Partial Domain Adaptation (LoCO-PDA): Enabling efficient CNN retraining on edge devices

no code implementations1 Mar 2022 Aditya Rajagopal, Christos-Savvas Bouganis

Consequently, it is likely that the observed data distribution upon deployment is a subset of the training data distribution.

Partial Domain Adaptation

perf4sight: A toolflow to model CNN training performance on Edge GPUs

1 code implementation12 Aug 2021 Aditya Rajagopal, Christos-Savvas Bouganis

The increased memory and processing capabilities of today's edge devices create opportunities for greater edge intelligence.

Multi-Precision Policy Enforced Training (MuPPET): A precision-switching strategy for quantised fixed-point training of CNNs

no code implementations16 Jun 2020 Aditya Rajagopal, Diederik Adriaan Vink, Stylianos I. Venieris, Christos-Savvas Bouganis

Large-scale convolutional neural networks (CNNs) suffer from very long training times, spanning from hours to weeks, limiting the productivity and experimentation of deep learning practitioners.

Now that I can see, I can improve: Enabling data-driven finetuning of CNNs on the edge

1 code implementation15 Jun 2020 Aditya Rajagopal, Christos-Savvas Bouganis

In today's world, a vast amount of data is being generated by edge devices that can be used as valuable training data to improve the performance of machine learning algorithms in terms of the achieved accuracy or to reduce the compute requirements of the model.

Approximate LSTMs for Time-Constrained Inference: Enabling Fast Reaction in Self-Driving Cars

no code implementations2 May 2019 Alexandros Kouris, Stylianos I. Venieris, Michail Rizakis, Christos-Savvas Bouganis

The need to recognise long-term dependencies in sequential data such as video streams has made Long Short-Term Memory (LSTM) networks a prominent Artificial Intelligence model for many emerging applications.

Autonomous Navigation Self-Driving Cars

DroNet: Efficient convolutional neural network detector for real-time UAV applications

2 code implementations18 Jul 2018 Christos Kyrkou, George Plastiras, Stylianos Venieris, Theocharis Theocharides, Christos-Savvas Bouganis

Through the analysis we propose a CNN architecture that is capable of detecting vehicles from aerial UAV images and can operate between 5-18 frames-per-second for a variety of platforms with an overall accuracy of ~95%.

Object Detection In Aerial Images One-Shot Object Detection +1

CascadeCNN: Pushing the Performance Limits of Quantisation in Convolutional Neural Networks

no code implementations13 Jul 2018 Alexandros Kouris, Stylianos I. Venieris, Christos-Savvas Bouganis

This work presents CascadeCNN, an automated toolflow that pushes the quantisation limits of any given CNN model, aiming to perform high-throughput inference.

Deploying Deep Neural Networks in the Embedded Space

no code implementations22 Jun 2018 Stylianos I. Venieris, Alexandros Kouris, Christos-Savvas Bouganis

Recently, Deep Neural Networks (DNNs) have emerged as the dominant model across various AI applications.

f-CNN$^{\text{x}}$: A Toolflow for Mapping Multi-CNN Applications on FPGAs

no code implementations25 May 2018 Stylianos I. Venieris, Christos-Savvas Bouganis

The predictive power of Convolutional Neural Networks (CNNs) has been an integral factor for emerging latency-sensitive applications, such as autonomous drones and vehicles.

CascadeCNN: Pushing the performance limits of quantisation

no code implementations22 May 2018 Alexandros Kouris, Stylianos I. Venieris, Christos-Savvas Bouganis

This work presents CascadeCNN, an automated toolflow that pushes the quantisation limits of any given CNN model, to perform high-throughput inference by exploiting the computation time-accuracy trade-off.

Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

no code implementations15 Mar 2018 Stylianos I. Venieris, Alexandros Kouris, Christos-Savvas Bouganis

In the past decade, Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance in various Artificial Intelligence tasks.

Approximate FPGA-based LSTMs under Computation Time Constraints

no code implementations7 Jan 2018 Michalis Rizakis, Stylianos I. Venieris, Alexandros Kouris, Christos-Savvas Bouganis

Recurrent Neural Networks and in particular Long Short-Term Memory (LSTM) networks have demonstrated state-of-the-art accuracy in several emerging Artificial Intelligence tasks.

Autonomous Vehicles Image Captioning +1

fpgaConvNet: A Toolflow for Mapping Diverse Convolutional Neural Networks on Embedded FPGAs

no code implementations23 Nov 2017 Stylianos I. Venieris, Christos-Savvas Bouganis

By selectively optimising for throughput, latency or multiobjective criteria, the presented tool is able to efficiently explore the design space and generate hardware designs from high-level ConvNet specifications, explicitly optimised for the performance metric of interest.

Robust Multi-Image Based Blind Face Hallucination

no code implementations CVPR 2015 Yonggang Jin, Christos-Savvas Bouganis

This paper proposes a robust multi-image based blind face hallucination framework to super-resolve LR faces.

Deblurring Face Hallucination +1

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