Department of Computer Engineering, University of Peradeniya
- 218 followers
- Peradeniya, Sri Lanka
- http://www.ce.pdn.ac.lk/
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Repositories
- e20-co502-RV32IM_Pipelined_Processor_Group-06 Public
RISC-V pipeline processor: A high-performance, open-source CPU design implementing RISC-V architecture with efficient instruction pipeline execution.
cepdnaclk/e20-co502-RV32IM_Pipelined_Processor_Group-06’s past year of commit activity - api.ce.pdn.ac.lk Public
API Portal of the Department of Computer Engineering https://api.ce.pdn.ac.lk/
cepdnaclk/api.ce.pdn.ac.lk’s past year of commit activity - people.ce.pdn.ac.lk Public
Student and staff profile website of the Department of Computer Engineering, University of Peradeniya https://people.ce.pdn.ac.lk/
cepdnaclk/people.ce.pdn.ac.lk’s past year of commit activity - e20-3yp-VR-Multiplayer-Golf-Game Public
VR-Multiplayer-Golf-Game allows multiple users to play golf in VR environment. The game features a virtual environment which allows a user to have an immersive experience.
cepdnaclk/e20-3yp-VR-Multiplayer-Golf-Game’s past year of commit activity - e20-co502-RV32IM_Pipelined_Processor_Group-05 Public
The RV32IM pipeline processor project designs a 32-bit RISC-V processor with 5 stages: IF, ID, EX, MEM, WB. It supports RV32I base and M-extension (MUL/DIV), using forwarding, stalling, and branch prediction to manage hazards. Implemented in Verilog, it is simulated, tested with RISC-V tools, and optimized for performance.
cepdnaclk/e20-co502-RV32IM_Pipelined_Processor_Group-05’s past year of commit activity - projects.ce.pdn.ac.lk Public
This is the student project portfolio website of the Department of Computer Engineering, University of Peradeniya. https://projects.ce.pdn.ac.lk
cepdnaclk/projects.ce.pdn.ac.lk’s past year of commit activity - e20-3yp-Smart-IOT-Indoor-Lighting-System Public
This Smart IoT Lighting System is designed for indoor lighting, integrating hardware with cloud connectivity for seamless control. Users can conveniently configure preferences and manage settings via a secure mobile app with voice command support, ensuring personalized and efficient lighting experiences, ease of use, and robust data security.
cepdnaclk/e20-3yp-Smart-IOT-Indoor-Lighting-System’s past year of commit activity - e20-co542-Panic-Attack-Prediction Public
Mental health disorders, including panic attacks, are rising globally. Traditional diagnosis relies on self-reports, which can be subjective. Using machine learning and neural networks, our project analyzes physiological and lifestyle data to predict panic attacks, enabling proactive management and intervention to improve mental well-being.
cepdnaclk/e20-co542-Panic-Attack-Prediction’s past year of commit activity
