Opening Thesis
3D Model Segmentation Leveraging 2D Cues and Representations
2025/09/08
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Dynamic Rank Allocation for Efficient LLM Inference on GPUs
2025/07/11
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Explainable AI for Efficient Inference on Hardware
2025/07/11
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LUT-based Neural Networks
2025/07/11
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When Variations Meet Early-Exit
2025/07/11
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Conversion of Convolutional Neural Networks (CNNs) into Logic Flows for Efficient Execution on RISC-V CPUs
2025/04/14
Master thesis
When CNNs are deployed on edge devices, often a huge number of dedicated hardware multiply-accumulate (MAC) units are not available to process massive MAC operations in CNNs. Instead, CPUs exist in nearly all these devices. CPUs themselves are not good at executing such mathematical operations on a large scale, since they opt more to execute control flow logic. To execute CNNs on CPUs eGiciently, it is critical to convert their MAC operations into logic flows. In this master thesis, the execution of a convolutional neural network (CNN) will be converted to logic flows, so that it can be executed with low latency and low energy on CPUs.
Exploring Robust Optical Accelerators for Neural Networks
2025/03/17
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Efficient LLM Inference with Weight Selection
2025/03/17
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Dynamic power allocation for hierarchical neural networks on FPGAs
2025/03/17
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Thesis Template
| All Students are required to use the following LaTex template for their thesis : Thesis Template |
Ongoing Master Theses
Enhancing Multi-View Editable Vector Graphics with Generative AI
2025/05/08
Supervisors: Mengnan Jiang, M.Sc., Prof. Dr.-Ing. Grace Li Zhang
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Finished Master Theses
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