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Hadi Mousanejad Jeddi is a PhD student at Linköping University specializing in scalable architectures for on-device inference and training of graph attention and convolutional networks. His research involves leveraging dynamic range analysis and efficient post-training quantization techniques for graph convolutional networks. He has contributed to publications in the IEEE Transactions on Large Scale Integration (VLSI) Systems and presented works at the IEEE Nordic Circuits Systems Conference, NORCAS. His academic endeavors focus on enhancing computational efficiency and performance in neural network architectures, particularly in embedded systems.
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