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Showing 1 to 3 of 3 for “"model parallelism"”.
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Exploring model parallelism in distributed scheduling of neural network frameworks
… that can no longer fit on a single machine. A model parallel approach, backed by partitioning of Neural Networks and placement of operators on devices in a distributed system, provides a better distributed solution to this problem. In this thesis, we motivate the case for device placement in …
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Understanding the Performance of Transformer Inference
… by scaling up transformer-based machine learning models, which can have more than a hundred billion parameters. Training and deploying these models can be difficult and extremely expensive, and performance engineering efforts to improve the latency and throughput of these models are crucial in …
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Distributed On-line Training for Object Detection on Embedded Devices.
… distributed approach for object detection model training and inference, using low cost embedded devices. Examples of the usage of such an approach is automatic beach litter collection using mobile robots, IoT based intelligent video surveillance system for traffic monitoring, indoor …