Jin-Wei Chang

Jin-Wei Chang received the M.S. degree in computer science at the Department of Computer Science, National Yang Ming Chiao Tung University. He received the B.S. degree in computer science from the Department of Computer Science and Engineering, Yuan Ze University. His research interests include emerging memory and storage technologies, embedded systems, and edge intelligence.

Publications

Master Thesis

A Hybrid Strategy for Dynamic Neural Network Inferencing on Energy-Harvesting Devices

https://hdl.handle.net/20.500.14371/3830

Journals

  1. Hong-Yi Chen, Jin-Wei Chang, Hong-Ruei Lin, and Li-Pin Chang, Graceful CNN Model Degradation in Uncorrected Flash Storage for Embedded Edge Devices, ACM Transactions on Storage (TOS), 22, 1, Article 4 (February 2026), 25 pages., DOI: 10.1145/3747298
  2. Jin-Wei Chang and Tseng-Yi Chen, When B+-tree Meets Skyrmion Memory: How Skyrmion Memory Affects an Indexing Scheme, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), vol. 41, no. 11, pp. 3814-3825, DOI: 10.1109/TCAD.2022.3197519

    (Integrated with ACM/IEEE CODES+ISSS 2022)

Conferences

  1. Jin-Wei Chang and Tseng-Yi Chen, When B+-tree Meets Skyrmion Memory: How Skyrmion Memory Affects an Indexing Scheme, ACM/IEEE CODES+ISSS 2022

    (Integrated with Computer-Aided Design of Integrated Circuits and Systems (TCAD))

Programming Projects

Skyrmion Racetrace Memory on B+ Tree Simulator

GitHubhttps://github.com/jinwei-chang/Skyrmion-on-B-plus-tree-Simulator

C++Simulator

Simulate the behaviors on B+ tree indexing scheme of the advanced memory material, skyrmion racetrack memory, and collect the memory operations to analyze the energy consumption and latency with different algorithms.

Verilog Simulation Optimization via Instruction Reduction

GitHubhttps://github.com/jinwei-chang/Verilog-Simulation-Optimization-via-Instruction-Reduction

C++Compiler

Reduce code size of verilog files and keep the input and output logic with same logic effect.

Dynamic Neural Networks on Energy Harvesting Devices

CC++PythonTensorFlowTI MSP430FR5994Arduino

The programming project for the thesis experiment. We implement the dynamic neural networks on the TI MSP430FR5994 evaluation board with SRAM and FRAM, and we design a circuit to simulate an energy harvesting environment.This project includes training a neural network, converting TensorFlow model to quantized model to fit on the MCU, and implementing dynamic neural network and hybrid strategy on the MCU.

EasyTools

🌐https://easytools1221.github.io

HTML5Tailwind CSSJavaScriptAstroPWAGoogle AnalyticsGoogle Adsense

My website hosts on the Github Page to provide some convenitent tools to help people solve problems in their lives.