This project develops a RISC-V SoC with an integrated Neuromorphic Accelerator for small-scale Spiking Neural Network (SNN) applications. The SoC is designed for low-power, low-latency edge computing, enabling efficient neuromorphic processing for embedded systems and real-time AI workloads.

Artery Resolver provides automated tools for accurately assessing arterial function by measuring changes in vessel diameter and blood velocity in response to blood flow changes. This project was completed in collaboration with the Medical Faculty of University of Peradeniya. Special thanks goes to Dr. Damsara Nandadeva and Dr. Sumudu Jayawardana.

API Portal of the Department of Computer Engineering https://api.ce.pdn.ac.lk/

The Smart Environmental Monitoring system will not only simplify and streamline plantation management operations but also enhance efficiency and promote sustainability. By offering real-time data collection and actionable insights, our solution will empower decision-making and optimize resource usage.

Student and staff profile website of the Department of Computer Engineering, University of Peradeniya https://people.ce.pdn.ac.lk/

This is the student project portfolio website of the Department of Computer Engineering, University of Peradeniya. https://projects.ce.pdn.ac.lk

This repository contains an implementation of a RISC-V RV32IM processor with a 6-stage pipeline architecture. The design includes instruction fetch, decode, execute, memory access, write-back, and an additional stage for improved performance. It supports integer operations, multiplication, and memory access as defined in the RV32IM instruction set.

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.