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Found 9 papers, 0 papers with code
Partitioning Distributed Compute Jobs with Reinforcement Learning and Graph Neural Networks
Many of these models are too large to be trained on a single machine, and instead must be distributed across multiple devices.
RAMP: A Flat Nanosecond Optical Network and MPI Operations for Distributed Deep Learning Systems
Distributed deep learning (DDL) systems strongly depend on network performance.
Network Aware Compute and Memory Allocation in Optically Composable Data Centres with Deep Reinforcement Learning and Graph Neural Networks
Resource-disaggregated data centre architectures promise a means of pooling resources remotely within data centres, allowing for both more flexibility and resource efficiency underlying the increasingly important infrastructure-as-a-service business.
One-shot, Offline and Production-Scalable PID Optimisation with Deep Reinforcement Learning
Furthermore, once trained (which takes $\mathcal{O}(hours)$), the model generates actuator-unique PID parameters in a one-shot inference process that takes $\mathcal{O}(ms)$ in comparison to up to $\mathcal{O}(week)$ required for conventional tuning methods, therefore accomplishing these performance improvements whilst achieving up to a $10^6\times$ speed-up.
Maximum Likelihood Estimation of Differentiated Products Demand Systems
We discuss estimation of the differentiated products demand system of Berry et al (1995) (BLP) by maximum likelihood estimation (MLE).
Resource Allocation in Disaggregated Data Centre Systems with Reinforcement Learning
Resource-disaggregated data centres (RDDC) propose a resource-centric, and high-utilisation architecture for data centres (DC), avoiding resource fragmentation and enabling arbitrarily sized resource pools to be allocated to tasks, rather than server-sized ones.
Random Walk for modelling Multi Core Fiber cross-talk and step distribution characterisation
In addition, this model is consistent with statistical analysis such as short term average crosstalk (STAXT), keeping the same convergence properties and it showed to be almost independent to time-window.
Experimental Analysis on Variations and Accuracy of Crosstalk in Trench-Assisted Multi-core Fibers
Space division multiplexing using multi-core fiber (MCF) is a promising solution to cope with the capacity crunch in standard single-mode fiber based optical communication systems.
Optimal Control of SOAs with Artificial Intelligence for Sub-Nanosecond Optical Switching
Novel approaches to switching ultra-fast semiconductor optical amplifiers using artificial intelligence algorithms (particle swarm optimisation, ant colony optimisation, and a genetic algorithm) are developed and applied both in simulation and experiment.
