Dynamic resource assignment, the real-time allocation of task streams to heterogeneous processing nodes, is the backbone of modern computing infrastructure. While learning-based schedulers excel in research, industrial deployments still rely on hand-written rules that operators can read, audit, and execute within tight...
Conventional CPU scheduling policies such as FirstCome First-Served (FCFS), Shortest Job First (SJF), Round Robin(RR), and Priority scheduling apply fixed, pre-defined rules thatare optimal only under specific workload conditions and cannotadapt to dynamic and heterogeneous execution environments. Thispaper presents an...
RunSoC 2.0 improves the realism of early-stage MPSoC analysis while retaining practical solution times for large automotive workloads, and incorporates cluster-aware communication and memory modeling.
INTRODUCTION: Edge-cloud schedulers must coordinate latency, energy, load balance, and deadline compliance under changing demand while keeping task-arrival and throughput units physically consistent.OBJECTIVE: This study evaluates MORL-ECSO under an auditable, paired-seed simulation protocol and compares it with tuned...
Task-based HPC runtimes are typically tuned under a performance-first requirement with the assumption that the fastest configuration requires all available cores. This paper asks whether that assumption holds, and whether high performance can be achieved with fewer resources. We study this question using SWITCHES, a ta...
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
Learn more about our approach to safety and security for Gemini 4 Argon.
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