Department of Computer Engineering, University of Peradeniya
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- 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.
- 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.
- Design and implementation of a 32-bit RISC-V processor supporting the RV32IM instruction set, developed as part of the Advanced Computer Architecture course (CO502). Webpage: https://cepdnaclk.github.io/e20-co502-RV32IM_Pipelined_Processor
- 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.
e20-3yp-Smart-Aquarium
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Public- 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.
e20-3yp-SkyT
Public- 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.
- This repo focuses on latency-aware resource optimization for Kubernetes
cepdnaclk.github.io
PublicGithub pages website for Department of Computer Engineering, University of Peradeniya. https://cepdnaclk.github.io- This project explores the development of AI-powered decision-making models to optimize corporate workflows, enhance strategic planning, and improve operational efficiency. By leveraging machine learning and natural language processing, the system will analyze corporate data including HR, finance, and operations to generate data-driven insights
- A Smart Garbage Sorting System using IoT to automate waste sorting, monitor bin statuses, and optimize garbage collection routes. The system uses sensors, GPS, and real-time alerts to improve efficiency, reduce costs, and promote sustainable waste management practices.
- This research is about developing an automated software tool that calculates the Peer Assessment Rating (PAR) Index to evaluate orthodontic treatment success. This project leverages 3D orthodontic study models (.stl files) and machine learning algorithms to create a cost-effective, accurate, and accessible evaluation system.