Search Results for author:
Found 19 papers, 5 papers with code
Multi-Precision Policy Enforced Training (MuPPET) : A Precision-Switching Strategy for Quantised Fixed-Point Training of CNNs
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
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.
Augmenting Softmax Information for Selective Classification with Out-of-Distribution Data
However, the performance of detection methods is generally evaluated on the task in isolation, rather than also considering potential downstream tasks in tandem.
Multi-DNN Accelerators for Next-Generation AI Systems
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
Consequently, it is likely that the observed data distribution upon deployment is a subset of the training data distribution.
perf4sight: A toolflow to model CNN training performance on Edge GPUs
The increased memory and processing capabilities of today's edge devices create opportunities for greater edge intelligence.
Caffe Barista: Brewing Caffe with FPGAs in the Training Loop
CNN training on FPGAs is a nascent field of research.
Multi-Precision Policy Enforced Training (MuPPET): A precision-switching strategy for quantised fixed-point training of CNNs
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
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
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.
DroNet: Efficient convolutional neural network detector for real-time UAV applications
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
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
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
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
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
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
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.
fpgaConvNet: A Toolflow for Mapping Diverse Convolutional Neural Networks on Embedded FPGAs
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
This paper proposes a robust multi-image based blind face hallucination framework to super-resolve LR faces.
