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Found 30 papers, 2 papers with code
NAWQ-SR: A Hybrid-Precision NPU Engine for Efficient On-Device Super-Resolution
In recent years, image and video delivery systems have begun integrating deep learning super-resolution (SR) approaches, leveraging their unprecedented visual enhancement capabilities while reducing reliance on networking conditions.
Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation
Recent image degradation estimation methods have enabled single-image super-resolution (SR) approaches to better upsample real-world images.
The Future of Consumer Edge-AI Computing
Deep Learning has proliferated dramatically across consumer devices in less than a decade, but has been largely powered through the hardware acceleration within isolated devices.
Fluid Batching: Exit-Aware Preemptive Serving of Early-Exit Neural Networks on Edge NPUs
With deep neural networks (DNNs) emerging as the backbone in a multitude of computer vision tasks, their adoption in real-world consumer applications broadens continuously.
Adaptable Butterfly Accelerator for Attention-based NNs via Hardware and Algorithm Co-design
By jointly optimizing the algorithm and hardware, our FPGA-based butterfly accelerator achieves 14. 2 to 23. 2 times speedup over state-of-the-art accelerators normalized to the same computational budget.
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.
How to Reach Real-Time AI on Consumer Devices? Solutions for Programmable and Custom Architectures
Collectively, these results highlight the critical need for further exploration as to how the various cross-stack solutions can be best combined in order to bring the latest advances in deep learning close to users, in a robust and efficient manner.
OODIn: An Optimised On-Device Inference Framework for Heterogeneous Mobile Devices
Radical progress in the field of deep learning (DL) has led to unprecedented accuracy in diverse inference tasks.
Multi-Exit Semantic Segmentation Networks
At the same time, the heterogeneous capabilities of the target platforms and the diverse constraints of different applications require the design and training of multiple target-specific segmentation models, leading to excessive maintenance costs.
Deep Neural Network-based Enhancement for Image and Video Streaming Systems: A Survey and Future Directions
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.
DynO: Dynamic Onloading of Deep Neural Networks from Cloud to Device
Recently, there has been an explosive growth of mobile and embedded applications using convolutional neural networks(CNNs).
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.
FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout
FjORD alleviates the problem of client system heterogeneity by tailoring the model width to the client's capabilities.
It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation
On-device machine learning is becoming a reality thanks to the availability of powerful hardware and model compression techniques.
Neural Enhancement in Content Delivery Systems: The State-of-the-Art and Future Directions
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.
SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud
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.
HAPI: Hardware-Aware Progressive Inference
Convolutional neural networks (CNNs) have recently become the state-of-the-art in a diversity of AI tasks.
Journey Towards Tiny Perceptual Super-Resolution
Recent works in single-image perceptual super-resolution (SR) have demonstrated unprecedented performance in generating realistic textures by means of deep convolutional networks.
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.
MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors
In recent years, convolutional networks have demonstrated unprecedented performance in the image restoration task of super-resolution (SR).
EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices
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.
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.
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.
