3.7 KiB
title, venue, year, date, tags, paper_count, draft
| title | venue | year | date | tags | paper_count | draft |
|---|---|---|---|---|---|---|
| SEC 2025 Digest | SEC | 2025 | 2025-01-01 | 12 | false |
12 papers selected.
lm-Meter: Unveiling Runtime Inference Latency for On-Device Language Models
Haoxin Wang 0003
TL;DR — Provides the first detailed runtime profiling framework for on-device LLM inference, revealing key latency bottlenecks across diverse edge hardware configurations.
SLED: A Speculative LLM Decoding Framework for Efficient Edge Serving
Xiangchen Li
TL;DR — Adapts speculative decoding to edge serving constraints, reducing LLM token generation latency while respecting the tight memory and compute budgets of edge nodes.
SledgeScale: Load-Aware Dispatch and Deadline-Driven Scheduling for Scalable, Dense Serverless Computing in Edge Data Centers
Xiaosu Lyu
TL;DR — Introduces a load-aware dispatch and deadline-driven scheduler for dense serverless edge data centers, demonstrating substantial SLA compliance improvements over baseline policies.
Warping the Edge: Enabling Instant Mobility for Stateful Applications over 5G and Beyond
Mukhtiar Ahmad
TL;DR — Achieves near-instantaneous stateful application migration across 5G edge nodes by combining memory snapshotting with network-layer forwarding continuity.
Uncertainty-Aware RL-Based Scheduling of Multi-DNN Workloads on Edge MPSoCs
Soroush Heidari
TL;DR — Uses uncertainty-aware reinforcement learning to schedule concurrent DNN workloads on heterogeneous edge MPSoCs, reducing deadline misses under dynamic arrival patterns.
SEEB-GPU: Early-Exit Aware Scheduling and Batching for Edge GPU Inference
Srinivasan Subramaniyan
TL;DR — Exploits early-exit branching in DNN inference to build an adaptive batching and scheduling policy for edge GPUs that cuts average latency without sacrificing throughput.
Elastoformer: Enabling Dynamic Adaptivity via Elastic Model Transformation
Sudaksh Kalra
TL;DR — Proposes elastic transformer transformations that resize model capacity at runtime to match available edge resources, enabling continuous inference under fluctuating conditions.
PlatformX: An End-to-End Transferable Platform for Energy-Efficient Neural Architecture Search
Xiaolong Tu
TL;DR — Presents a transferable NAS platform that searches for energy-efficient DNN architectures deployable across heterogeneous edge targets with minimal re-search overhead.
Bayes-Split-Edge: Bayesian Optimization for Constrained Collaborative Inference in Wireless Edge Systems
Fatemeh Zahra Safaeipour
TL;DR — Applies Bayesian optimization to find optimal split points for collaborative inference in wireless edge systems, accounting for dynamic channel and computation constraints.
Energy-efficient DNN Dividing Technique for Latency Optimization in Dynamic Mobile Edge Networks
Eldiyar Zhantileuov
TL;DR — Develops a DNN partitioning strategy for mobile edge networks that minimizes end-to-end latency while satisfying energy budgets under time-varying link conditions.
LLM-Driven Auto Configuration for Transient IoT Device Collaboration
Hetvi Shastri
TL;DR — Leverages LLMs to automate the configuration of transient IoT device coalitions, reducing manual setup overhead and adapting collaboration policies to changing device membership.
fReeLoaders: An IoT Ecosystem for Real-Time Deadline-Driven Task Scheduling using Reinforcement Learning
Marshall Clyburn
TL;DR — Builds a reinforcement-learning scheduler for IoT ecosystems that meets real-time task deadlines by exploiting opportunistic idle capacity across heterogeneous edge devices.