11 |
Training of Object Detection Spiking Neural Networks for Event-Based VisionJohansson, Olof January 2021 (has links)
Event-based vision offers high dynamic range, time resolution and lower latency than conventional frame-based vision sensors. These attributes are useful in varying light condition and fast motion. However, there are no neural network models and training protocols optimized for object detection with event data, and conventional artificial neural networks for frame-based data are not directly suitable for that task. Spiking neural networks are natural candidates but further work is required to develop an efficient object detection architecture and end-to-end training protocol. For example, object detection in varying light conditions is identified as a challenging problem for the automation of construction equipment such as earth-moving machines, aiming to increase the safety of operators and make repetitive processes less tedious. This work focuses on the development and evaluation of a neural network for object detection with data from an event-based sensor. Furthermore, the strengths and weaknesses of an event-based vision solution are discussed in relation to the known challenges described in former works on automation of earth-moving machines. A solution for object detection with event data is implemented as a modified YOLOv3 network with spiking convolutional layers trained with a backpropagation algorithm adapted for spiking neural networks. The performance is evaluated on the N-Caltech101 dataset with classes for airplanes and motorbikes, resulting in a mAP of 95.8% for the combined network and 98.8% for the original YOLOv3 network with the same architecture. The solution is investigated as a proof of concept and suggestions for further work is described based on a recurrent spiking neural network.
|
12 |
A High-Level Interface for Accelerating Spiking Neural Networks on the Edge with Heterogeneous Hardware : Enabling Rapid Prototyping of Training Algorithms and Topologies on Field-Programmable Gate ArraysEidlitz Rivera, Kaspar Oscarsson January 2024 (has links)
With the increasing use of machine learning by devices at the network's edge, a trend of moving computation from data centers to these devices is emerging. This shift imposes strict energy requirements on the algorithms used and the hardware on which they are implemented. Neuromorphic spiking neural networks (SNNs) and heterogeneous sytems on a chip (SoCs) are showing great potential for energy-efficient computing on the edge. This thesis describes the development of a high-level interface for accelerating SNNs on an FPGA–CPU SoC. The system is based on an existing open-source, low-level implementation, adapting it for a research-focused Python front-end. The developed interface provides a productive environment for exploring and evaluating SNN algorithms and topologies through compatibility with industry-standard tools for numerical computing, data analysis, and visualization, while still taking full advantage of FPGA-based hardware acceleration. The system is evaluated and showcased by analyzing the training of a small network to solve the XOR problem. As the project matures, future development could enable integration with commonly used machine learning libraries, further increasing it's potential.
|
13 |
Leakage-Current-Aware Layout Design of DNTT-Based OTFTs and Its Applications to Digital Circuits / DNTTを用いる有機薄膜トランジスタのリーク電流考慮レイアウト設計とそのデジタル回路への応用Oshima, Kunihiro 25 March 2024 (has links)
京都大学 / 新制・課程博士 / 博士(情報学) / 甲第25444号 / 情博第882号 / 新制||情||148(附属図書館) / 京都大学大学院情報学研究科通信情報システム専攻 / (主査)教授 佐藤 高史, 教授 橋本 昌宜, 教授 新津 葵一 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM
|
Page generated in 0.018 seconds