Opening Thesis

• Conversion of Convolutional Neural Networks (CNNs) into Logic Flows for E9icient 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.

  Announcement as PDF

• Exploring Robust Optical Accelerators for Neural Networks

  2025/03/17

  Master thesis

  Supervisor: Prof. Dr.-Ing. Grace Li Zhang

  Announcement as PDF

• Enhancing Multi-View Editable Vector Graphics with Generative AI

  2025/03/17

  Master thesis

  Supervisors: Mengnan Jiang, M.Sc., Prof. Dr.-Ing. Grace Li Zhang

  Announcement as PDF

• Efficient LLM Inference with Weight Selection

  2025/03/17

  Master thesis

  Supervisors: Jingcun Wang, M.Sc., Prof. Dr.-Ing. Grace Li Zhang

  Announcement as PDF

• Dynamic power allocation for hierarchical neural networks on FPGAs

  2025/03/17

  Master thesis

  Supervisor: Prof. Dr.-Ing. Grace Li Zhang

  Announcement as PDF

• Converting CNNs into Digital Logic with Class Importance

  2024/12/02

  Master thesis

  Supervisor: Prof. Dr.-Ing. Grace Li Zhang

  Announcement as PDF

• Automatic combinational circuit generation with neural networks

  2024/12/02

  Master thesis

  Supervisor: Prof. Dr.-Ing. Grace Li Zhang

  Announcement as PDF