12 citations · 47 across the 21 of their papers we have counts for
22 papers
MANE: A Multi-Path Adaptive Network for Edge Onloading of Deep Neural Networks
Sokratis Nikolaidis, Stylianos I. Venieris, Leonidas Malachias +1
Split computing constitutes a widely used distributed inference approach, where a lightweight head model is onloaded onto the device and a heavier tail model resides on an edge ser…
Margins, Not Windows: Training-Free Per-Step Lossy Speculative Decoding
Oszkár Urbán, Young D. Kwon, Stylianos I. Venieris +1
Speculative decoding accelerates LLM inference by drafting candidate tokens and verifying them in parallel. Tree-attention drafters such as EAGLE-3 are widely adopted, yet typicall…
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)…
A-THENA: Early Intrusion Detection for IoT with Time-Aware Hybrid Encoding and Network-Specific Augmentation
Ioannis Panopoulos, Maria Lamprini A. Bartsioka, Sokratis Nikolaidis +3
The proliferation of Internet of Things (IoT) devices has significantly expanded attack surfaces, making IoT ecosystems particularly susceptible to sophisticated cyber threats. To…
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…
HierarchicalPrune: Position-Aware Compression for Large-Scale Diffusion Models
Young D. Kwon, Rui Li, Sijia Li +3
State-of-the-art text-to-image diffusion models (DMs) achieve remarkable quality, yet their massive parameter scale (8-11B) poses significant challenges for inferences on resource-…
Dynamic Temporal Positional Encodings for Early Intrusion Detection in IoT
Ioannis Panopoulos, Maria-Lamprini A. Bartsioka, Sokratis Nikolaidis +3
The rapid expansion of the Internet of Things (IoT) has introduced significant security challenges, necessitating efficient and adaptive Intrusion Detection Systems (IDS). Traditio…
FedPEFT: Federated Learning to Personalize PEFT for Multilingual LLMs
Royson Lee, Minyoung Kim, Fady Rezk +3
Federated learning (FL) has enabled the training of multilingual large language models (LLMs) on diverse and decentralized multilingual data, especially on low-resource languages.…
MultiTASC++: A Continuously Adaptive Scheduler for Edge-Based Multi-Device Cascade Inference
Sokratis Nikolaidis, Stylianos I. Venieris, Iakovos S. Venieris
Cascade systems, consisting of a lightweight model processing all samples and a heavier, high-accuracy model refining challenging samples, have become a widely-adopted distributed…
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…
CARIn: Constraint-Aware and Responsive Inference on Heterogeneous Devices for Single- and Multi-DNN Workloads
Ioannis Panopoulos, Stylianos I. Venieris, Iakovos S. Venieris
The relentless expansion of deep learning applications in recent years has prompted a pivotal shift toward on-device execution, driven by the urgent need for real-time processing,…
Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference
Hao Mark Chen, Wayne Luk, Ka Fai Cedric Yiu +4
The auto-regressive decoding of Large Language Models (LLMs) results in significant overheads in their hardware performance. While recent research has investigated various speculat…
LifeLearner: Hardware-Aware Meta Continual Learning System for Embedded Computing Platforms
Young D. Kwon, Jagmohan Chauhan, Hong Jia +2
Continual Learning (CL) allows applications such as user personalization and household robots to learn on the fly and adapt to context. This is an important feature when context, a…
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…
Mitigating Memory Wall Effects in CNN Engines with On-the-Fly Weights Generation
Stylianos I. Venieris, Javier Fernandez-Marques, Nicholas D. Lane
The unprecedented accuracy of convolutional neural networks (CNNs) across a broad range of AI tasks has led to their widespread deployment in mobile and embedded settings. In a pur…
TinyTrain: Resource-Aware Task-Adaptive Sparse Training of DNNs at the Data-Scarce Edge
Young D. Kwon, Rui Li, Stylianos I. Venieris +3
On-device training is essential for user personalisation and privacy. With the pervasiveness of IoT devices and microcontroller units (MCUs), this task becomes more challenging due…
NAWQ-SR: A Hybrid-Precision NPU Engine for Efficient On-Device Super-Resolution
Stylianos I. Venieris, Mario Almeida, Royson Lee +1
In recent years, image and video delivery systems have begun integrating deep learning super-resolution (SR) approaches, leveraging their unprecedented visual enhancement capabilit…
Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation
Royson Lee, Rui Li, Stylianos I. Venieris +3
Recent image degradation estimation methods have enabled single-image super-resolution (SR) approaches to better upsample real-world images. Among these methods, explicit kernel es…
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…
How to Reach Real-Time AI on Consumer Devices? Solutions for Programmable and Custom Architectures
Stylianos I. Venieris, Ioannis Panopoulos, Ilias Leontiadis +1
The unprecedented performance of deep neural networks (DNNs) has led to large strides in various Artificial Intelligence (AI) inference tasks, such as object and speech recognition…