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Found 12 papers, 1 papers with code
Sparse-DySta: Sparsity-Aware Dynamic and Static Scheduling for Sparse Multi-DNN Workloads
Running multiple deep neural networks (DNNs) in parallel has become an emerging workload in both edge devices, such as mobile phones where multiple tasks serve a single user for daily activities, and data centers, where various requests are raised from millions of users, as seen with large language models.
The Future of Consumer Edge-AI Computing
In the last decade, Deep Learning has rapidly infiltrated the consumer end, mainly thanks to hardware acceleration across 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 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.
Adaptive Inference through Early-Exit Networks: Design, Challenges and Directions
DNNs are becoming less and less over-parametrised due to recent advances in efficient model design, through careful hand-crafted or NAS-based methods.
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.
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.
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.
