106 citations · 154 across the 15 of their papers we have counts for
16 papers
Decoupling the Declarative from the Procedural in Vision-Language-Action Models
Nikolaos Tsagkas, Andreas Sochopoulos, Chris Xiaoxuan Lu +2
Deploying generalist robotic agents in the real world requires transferable skills. Specifically, a policy trained to clone a behavior from object-specific demonstrations must gene…
WhiFlash: Accelerating Speculative Decoding with Token-Level Cross-Paradigm Routing
Young D. Kwon, Miles Williams, Rui Li +2
The autoregressive nature of large language models (LLMs) remains a significant bottleneck for inference, particularly in complex agentic workloads. While speculative decoding (SD)…
Speculative Decoding with a Speculative Vocabulary
Miles Williams, Young D. Kwon, Rui Li +2
Speculative decoding has rapidly emerged as a leading approach for accelerating language model (LM) inference, as it offers substantial speedups while yielding identical outputs. T…
Progressive Mixed-Precision Decoding for Efficient LLM Inference
Hao Mark Chen, Fuwen Tan, Alexandros Kouris +3
In spite of the great potential of large language models (LLMs) across various tasks, their deployment on resource-constrained devices remains challenging due to their excessive co…
Sparse-DySta: Sparsity-Aware Dynamic and Static Scheduling for Sparse Multi-DNN Workloads
Hongxiang Fan, Stylianos I. Venieris, Alexandros Kouris +1
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 da…
The Future of Consumer Edge-AI Computing
Stefanos Laskaridis, Stylianos I. Venieris, Alexandros Kouris +2
In the last decade, Deep Learning has rapidly infiltrated the consumer end, mainly thanks to hardware acceleration across devices. However, as we look towards the future, it is evi…
Adaptable Butterfly Accelerator for Attention-based NNs via Hardware and Algorithm Co-design
Hongxiang Fan, Thomas Chau, Stylianos I. Venieris +5
Attention-based neural networks have become pervasive in many AI tasks. Despite their excellent algorithmic performance, the use of the attention mechanism and feed-forward network…
Fluid Batching: Exit-Aware Preemptive Serving of Early-Exit Neural Networks on Edge NPUs
Alexandros Kouris, Stylianos I. Venieris, Stefanos Laskaridis +1
With deep neural networks (DNNs) emerging as the backbone in a multitude of computer vision tasks, their adoption in real-world applications broadens continuously. Given the abunda…
Adaptive Inference through Early-Exit Networks: Design, Challenges and Directions
Stefanos Laskaridis, Alexandros Kouris, Nicholas D. Lane
DNNs are becoming less and less over-parametrised due to recent advances in efficient model design, through careful hand-crafted or NAS-based methods. Relying on the fact that not…
Multi-Exit Semantic Segmentation Networks
Alexandros Kouris, Stylianos I. Venieris, Stefanos Laskaridis +1
Semantic segmentation arises as the backbone of many vision systems, spanning from self-driving cars and robot navigation to augmented reality and teleconferencing. Frequently oper…
Approximate LSTMs for Time-Constrained Inference: Enabling Fast Reaction in Self-Driving Cars
Alexandros Kouris, Stylianos I. Venieris, Michail Rizakis +1
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…
CascadeCNN: Pushing the Performance Limits of Quantisation in Convolutional Neural Networks
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. A two-stage architectu…
Deploying Deep Neural Networks in the Embedded Space
Stylianos I. Venieris, Alexandros Kouris, Christos-Savvas Bouganis
Recently, Deep Neural Networks (DNNs) have emerged as the dominant model across various AI applications. In the era of IoT and mobile systems, the efficient deployment of DNNs on e…
CascadeCNN: Pushing the performance limits of quantisation
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…
Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions
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. To accelerate the experimentation…
Approximate FPGA-based LSTMs under Computation Time Constraints
Michalis Rizakis, Stylianos I. Venieris, Alexandros Kouris +1
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. Ho…