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09/04/2023
SATAY: A Streaming Architecture Toolflow for Accelerating YOLO Models on FPGA Devices
AI has led to significant advancements in computer vision and image proc...
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07/15/2023
PASS: Exploiting Post-Activation Sparsity in Streaming Architectures for CNN Acceleration
With the ever-growing popularity of Artificial Intelligence, there is an...
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06/08/2023
Mixed-TD: Efficient Neural Network Accelerator with Layer-Specific Tensor Decomposition
Neural Network designs are quite diverse, from VGG-style to ResNet-style...
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05/31/2023
fpgaHART: A toolflow for throughput-oriented acceleration of 3D CNNs for HAR onto FPGAs
Surveillance systems, autonomous vehicles, human monitoring systems, and...
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05/29/2023
FMM-X3D: FPGA-based modeling and mapping of X3D for Human Action Recognition
3D Convolutional Neural Networks are gaining increasing attention from r...
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04/17/2023
ATHEENA: A Toolflow for Hardware Early-Exit Network Automation
The continued need for improvements in accuracy, throughput, and efficie...
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03/30/2023
HARFLOW3D: A Latency-Oriented 3D-CNN Accelerator Toolflow for HAR on FPGA Devices
For Human Action Recognition tasks (HAR), 3D Convolutional Neural Networ...
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03/14/2023
Window-Based Early-Exit Cascades for Uncertainty Estimation: When Deep Ensembles are More Efficient than Single Models
Deep Ensembles are a simple, reliable, and effective method of improving...
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08/22/2022
SVD-NAS: Coupling Low-Rank Approximation and Neural Architecture Search
The task of compressing pre-trained Deep Neural Networks has attracted w...
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07/15/2022
On the Usefulness of Deep Ensemble Diversity for Out-of-Distribution Detection
The ability to detect Out-of-Distribution (OOD) data is important in saf...
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07/15/2022
Augmenting Softmax Information for Selective Classification with Out-of-Distribution Data
Detecting out-of-distribution (OOD) data is a task that is receiving an ...
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05/19/2022
Multi-DNN Accelerators for Next-Generation AI Systems
As the use of AI-powered applications widens across multiple domains, so...
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03/01/2022
Low-Cost On-device Partial Domain Adaptation (LoCO-PDA): Enabling efficient CNN retraining on edge devices
With the increased deployment of Convolutional Neural Networks (CNNs) on...
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11/30/2021
SAMO: Optimised Mapping of Convolutional Neural Networks to Streaming Architectures
Toolflows that map Convolutional Neural Network (CNN) models to Field Pr...
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08/12/2021
perf4sight: A toolflow to model CNN training performance on Edge GPUs
The increased memory and processing capabilities of today's edge devices...
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07/21/2021
Performance landscape of resource-constrained platforms targeting DNNs
Over the recent years, a significant number of complex, deep neural netw...
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06/18/2020
Caffe Barista: Brewing Caffe with FPGAs in the Training Loop
As the complexity of deep learning (DL) models increases, their compute ...
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06/16/2020
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 t...
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06/15/2020
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 devic...
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05/02/2019
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 ...
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02/13/2019
A Scalable FPGA-based Architecture for Depth Estimation in SLAM
The current state of the art of Simultaneous Localisation and Mapping, o...
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07/18/2018
DroNet: Efficient convolutional neural network detector for real-time UAV applications
Unmanned Aerial Vehicles (drones) are emerging as a promising technology...
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07/13/2018
CascadeCNN: Pushing the Performance Limits of Quantisation in Convolutional Neural Networks
This work presents CascadeCNN, an automated toolflow that pushes the qua...
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06/22/2018
Deploying Deep Neural Networks in the Embedded Space
Recently, Deep Neural Networks (DNNs) have emerged as the dominant model...
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05/25/2018
f-CNN^x: A Toolflow for Mapping Multiple Convolutional Neural Networks on FPGAs
The predictive power of Convolutional Neural Networks (CNNs) has been an...
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05/22/2018
CascadeCNN: Pushing the performance limits of quantisation
This work presents CascadeCNN, an automated toolflow that pushes the qua...
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03/15/2018
Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions
In the past decade, Convolutional Neural Networks (CNNs) have demonstrat...
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01/07/2018
Approximate FPGA-based LSTMs under Computation Time Constraints
Recurrent Neural Networks and in particular Long Short-Term Memory (LSTM...
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11/23/2017