Papers Scheduling
“Scheduling” 태그가 달린 논문 3,104편 · 필터 해제
An End-to-End DNN Inference Framework for the SpiNNaker2 Neuromorphic MPSoC
This work presents a multi-layer DNN scheduling framework as an extension of OctopuScheduler, providing an end-to-end flow from PyTorch models to inference on a single SpiNNaker2 chip. Together with a front-end comprised…
QuantizationSchedulingFremer: Lightweight and Effective Frequency Transformer for Workload Forecasting in Cloud Services
Workload forecasting is pivotal in cloud service applications, such as auto-scaling and scheduling, with profound implications for operational efficiency. Although Transformer-based forecasting models have demonstrated r…
Computational EfficiencySchedulingTransient-Stability-Aware Frequency Provision in IBR-Rich Grids via Information Gap Decision Theory and Deep Learning
This paper introduces a framework to address the critical loss of transient stability caused by reduced inertia in grids with high inverter-based resource (IBR) penetration. The proposed method integrates a predictive de…
SchedulingLeveraging Asynchronous Cross-border Market Data for Improved Day-Ahead Electricity Price Forecasting in European Markets
Accurate short-term electricity price forecasting is crucial for strategically scheduling demand and generation bids in day-ahead markets. While data-driven techniques have shown considerable prowess in achieving high fo…
SchedulingFormulaOne: Measuring the Depth of Algorithmic Reasoning Beyond Competitive Programming
Frontier AI models demonstrate formidable breadth of knowledge. But how close are they to true human -- or superhuman -- expertise? Genuine experts can tackle the hardest problems and push the boundaries of scientific un…
SchedulingAutonomous Resource Management in Microservice Systems via Reinforcement Learning
This paper proposes a reinforcement learning-based method for microservice resource scheduling and optimization, aiming to address issues such as uneven resource allocation, high latency, and insufficient throughput in t…
Managementreinforcement-learningReinforcement LearningSchedulingSystolicAttention: Fusing FlashAttention within a Single Systolic Array
Transformer models rely heavily on scaled dot-product attention (SDPA), typically implemented using the FlashAttention algorithm. However, current systolic-array-based accelerators face significant challenges when execut…
SchedulingOptimal Sensor Scheduling and Selection for Continuous-Discrete Kalman Filtering with Auxiliary Dynamics
We study the Continuous-Discrete Kalman Filter (CD-KF) for State-Space Models (SSMs) where continuous-time dynamics are observed via multiple sensors with discrete, irregularly timed measurements. Our focus extends to sc…
SchedulingState Space ModelsElk: Exploring the Efficiency of Inter-core Connected AI Chips with Deep Learning Compiler Techniques
To meet the increasing demand of deep learning (DL) models, AI chips are employing both off-chip memory (e.g., HBM) and high-bandwidth low-latency interconnect for direct inter-core data exchange. However, it is not easy…
SchedulingBitParticle: Partializing Sparse Dual-Factors to Build Quasi-Synchronizing MAC Arrays for Energy-efficient DNNs
Bit-level sparsity in quantized deep neural networks (DNNs) offers significant potential for optimizing Multiply-Accumulate (MAC) operations. However, two key challenges still limit its practical exploitation. First, con…
SchedulingAn Adaptive Volatility-based Learning Rate Scheduler
Effective learning rate (LR) scheduling is crucial for training deep neural networks. However, popular pre-defined and adaptive schedulers can still lead to suboptimal generalization. This paper introduces VolSched, a no…
SchedulingOpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety
Recent advances in AI agents capable of solving complex, everyday tasks, from scheduling to customer service, have enabled deployment in real-world settings, but their possibilities for unsafe behavior demands rigorous e…
AI AgentSchedulingHierarchical Task Offloading for UAV-Assisted Vehicular Edge Computing via Deep Reinforcement Learning
With the emergence of compute-intensive and delay-sensitive applications in vehicular networks, unmanned aerial vehicles (UAVs) have emerged as a promising complement for vehicular edge computing due to the high mobility…
Deep Reinforcement LearningEdge-computingSchedulingTrajectory PlanningANN-Based Grid Impedance Estimation for Adaptive Gain Scheduling in VSG Under Dynamic Grid Conditions
In contrast to grid-following inverters, Virtual Synchronous Generators (VSGs) perform well under weak grid conditions but may become unstable when the grid is strong. Grid strength depends on grid impedance, which unfor…
SchedulingJoint Scheduling of DER under Demand Charges: Structure and Approximation
We study the joint scheduling of behind-the-meter distributed energy resources (DERs), including flexible loads, renewable generation, and battery energy storage systems, under net energy metering frameworks with demand …
SchedulingCo-Design of Sensing, Communications, and Control for Low-Altitude Wireless Networks
The rapid advancement of Internet of Things (IoT) services and the evolution toward the sixth generation (6G) have positioned unmanned aerial vehicles (UAVs) as critical enablers of low-altitude wireless networks (LAWNs)…
SchedulingImproving Diffusion-Based Image Editing Faithfulness via Guidance and Scheduling
Text-guided diffusion models have become essential for high-quality image synthesis, enabling dynamic image editing. In image editing, two crucial aspects are editability, which determines the extent of modification, and…
Image GenerationSchedulingPredictive Maintenance Optimization for Smart Vending Machines Using IoT and Machine Learning
The increasing proliferation of vending machines in public and commercial environments has placed a growing emphasis on operational efficiency and customer satisfaction. Traditional maintenance approaches either reactive…
Fault DetectionSchedulingMAGPIE: A dataset for Multi-AGent contextual PrIvacy Evaluation
The proliferation of LLM-based agents has led to increasing deployment of inter-agent collaboration for tasks like scheduling, negotiation, resource allocation etc. In such systems, privacy is critical, as agents often a…
SchedulingMILAAP: Mobile Link Allocation via Attention-based Prediction
Channel hopping (CS) communication systems must adapt to interference changes in the wireless network and to node mobility for maintaining throughput efficiency. Optimal scheduling requires up-to-date network state infor…
PredictionScheduling