Vincent Lannurien
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109 lines
3.7 KiB
Markdown
109 lines
3.7 KiB
Markdown
---
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title: SEC 2025 Digest
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venue: SEC
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year: 2025
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date: '2025-01-01'
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tags: []
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paper_count: 12
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draft: false
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---
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12 papers selected.
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### lm-Meter: Unveiling Runtime Inference Latency for On-Device Language Models
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*Haoxin Wang 0003*
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**TL;DR** — Provides the first detailed runtime profiling framework for on-device LLM inference, revealing key latency bottlenecks across diverse edge hardware configurations.
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### SLED: A Speculative LLM Decoding Framework for Efficient Edge Serving
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*Xiangchen Li*
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**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.
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### SledgeScale: Load-Aware Dispatch and Deadline-Driven Scheduling for Scalable, Dense Serverless Computing in Edge Data Centers
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*Xiaosu Lyu*
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**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.
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### Warping the Edge: Enabling Instant Mobility for Stateful Applications over 5G and Beyond
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*Mukhtiar Ahmad*
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**TL;DR** — Achieves near-instantaneous stateful application migration across 5G edge nodes by combining memory snapshotting with network-layer forwarding continuity.
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### Uncertainty-Aware RL-Based Scheduling of Multi-DNN Workloads on Edge MPSoCs
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*Soroush Heidari*
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**TL;DR** — Uses uncertainty-aware reinforcement learning to schedule concurrent DNN workloads on heterogeneous edge MPSoCs, reducing deadline misses under dynamic arrival patterns.
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### SEEB-GPU: Early-Exit Aware Scheduling and Batching for Edge GPU Inference
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*Srinivasan Subramaniyan*
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**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.
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### Elastoformer: Enabling Dynamic Adaptivity via Elastic Model Transformation
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*Sudaksh Kalra*
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**TL;DR** — Proposes elastic transformer transformations that resize model capacity at runtime to match available edge resources, enabling continuous inference under fluctuating conditions.
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### PlatformX: An End-to-End Transferable Platform for Energy-Efficient Neural Architecture Search
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*Xiaolong Tu*
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**TL;DR** — Presents a transferable NAS platform that searches for energy-efficient DNN architectures deployable across heterogeneous edge targets with minimal re-search overhead.
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### Bayes-Split-Edge: Bayesian Optimization for Constrained Collaborative Inference in Wireless Edge Systems
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*Fatemeh Zahra Safaeipour*
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**TL;DR** — Applies Bayesian optimization to find optimal split points for collaborative inference in wireless edge systems, accounting for dynamic channel and computation constraints.
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### Energy-efficient DNN Dividing Technique for Latency Optimization in Dynamic Mobile Edge Networks
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*Eldiyar Zhantileuov*
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**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.
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### LLM-Driven Auto Configuration for Transient IoT Device Collaboration
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*Hetvi Shastri*
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**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.
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### fReeLoaders: An IoT Ecosystem for Real-Time Deadline-Driven Task Scheduling using Reinforcement Learning
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*Marshall Clyburn*
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**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.
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