Global ETD Search
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Showing 1 to 5 of 5 for “"in-network computing"”.
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A cross-stack, network-centric architectural design for next-generation datacenters
… cross-layer datacenter architecture based on in-network computing and near-memory processing paradigms. The proposed datacenter architecture is built atop two principles: (1) utilizing commodity, off-the-shelf hardware (i.e., processor, DRAM, and network devices) with minimal changes to their …
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Efficient Network Systems Design for Machine Learning
Machine learning (ML) is transforming modern life by powering a diverse range of groundbreaking applications. As ML models and datasets expand, the scale of training and inference workloads in modern datacenters is increasing at an unprecedented pace. As the demand for computing resources grows, …
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Accelerating distributed neural network training with network-centric approach
Distributed training of Deep Neural Networks (DNN) is an important technique to reduce the training time of large DNNs for a wide range of applications. In existing distributed training approaches, however, the communication time to periodically exchange parameters (i.e., weights) and gradients …
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Machine Learning Approach for Resource Allocation in Mobile Edge Computing
<p>Mobile Edge Computing (MEC) is recognized as a pivotal technology supporting cloud computing and innovative services at the network edge, offering significant reductions in system delay and mitigating network traffic congestion. It supports latency-sensitive applications like Augmented Reality …
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Communication-centric cross-stack acceleration for distributed machine learning
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms