AY 2019 GT Topics
1. Design and Evaluation of a Spiking Neuron Processing Core (SNPC) Based on Crossbar and Spike-Time-Dependent-Plasticty†
On-Device AI Hardware and Sofwatre Optimization†
2. Intelligent Recognition of Traffic Hand Sign Command Based on Convolution Neural Network System†
3. Efficient Convolution Neural Network Architecture Optimization on FPGA†
In this project, students implement an experimental environment using Autoware that is the open source platform realizes automatic driving.
- Development of a compact vehicle on which various sensors can be placed
- Creation of ROS module connecting sensor and Autoware,
- Electronic control of the produced vehicle
- Implementation of 3-D point cloud matching on FPGA
- Evaluation of Depth camera for visual SLAM
- Implementation of self-driving controller using Network on chip
Reference†
In this project, students implements small systemsusing Spiking Neural Network (SNN)
and/or binarized neural networks(BNN).
- Implementation of flight controller for quad-copter using pulse frequency modulation
- Evaluation of simple Spiking neural networks using MNIST dataset
- Traffic sign detection using binarized neural network
- Implementation of Kalman filter for neural engineering framework
Reference†
Cameras have the most information quantity among the sensors.
However, processing load increases to extract useful information from images.
Students implement algorithms to reduce processing load and processing time as possible.
- Noise elimination using TV-L1 regularization on FPGA
- Efficient algorithm for depth estimation from ego-motion
- Implementation of real-time semi-visual odometetory using FPGA
- Video classification using TSCDP and SVM
References†
![[PukiWiki]](image/pukiwiki.png "[PukiWiki]")
