AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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...
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Education Innovation
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
A Blog post by NVIDIA on Hugging Face
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