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A Survey of Distributed Learning in Cloud, Mobile, and Edge Settings
In the era of deep learning (DL), convolutional neural networks (CNNs), and large language models (LLMs), machine learning (ML) models are becoming increasingly complex, demanding significant computational resources for both inference and training stages.
MAFAT: Memory-Aware Fusing and Tiling of Neural Networks for Accelerated Edge Inference
Distributed partitioning approaches can, however, also be used to run in a reduced memory footprint on a single device by subdividing the network into smaller operations.
Virtual-Link: A Scalable Multi-Producer, Multi-Consumer Message Queue Architecture for Cross-Core Communication
Cross-core communication is increasingly a bottleneck as the number of processing elements increase per system-on-chip.
Hardware Architecture
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