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-…