A5039886741- Radiation Effects in Electronics
- Embedded Systems Design Techniques
- Advanced Neural Network Applications
- Low-power high-performance VLSI design
- VLSI and Analog Circuit Testing
- Interconnection Networks and Systems
- Advanced Memory and Neural Computing
- CCD and CMOS Imaging Sensors
- Parallel Computing and Optimization Techniques
- Integrated Circuits and Semiconductor Failure Analysis
- Industrial Vision Systems and Defect Detection
- Manufacturing Process and Optimization
- Modular Robots and Swarm Intelligence
Technische Universität Braunschweig
2023-2025
Modern and future AI-based automotive applications, such as autonomous driving, require the efficient real-time processing of huge amounts data from different sensors, like camera, radar, LiDAR. In ZuSE-KI-AVF project, multiple university, industry partners collaborate to develop a novel massive parallel processor architecture, based on cus-tomized RISC-V host processor, an high-performance vertical vector coprocessor. addition, software development framework is also provided efficiently...
Bit errors due to radiation effects are becoming increasingly important as the fabrication technologies shrinking with every generation of integrated circuits. The resulting smaller transistors more prone high-energy irradiation. This is relevant in avionics or even automotive, where safety millions cars must be ensured. paper proposes an experiment, multiple FPGAs (Field Programmable Gate Arrays) exposed 2.45MeV neutron irradiation parallel. Bitflips different memory components (Block RAM,...
When using Field-Programmable Gate Arrays (FPGA) in safety-critical and harsh environments, it is important to understand possible faults implement appropriate mitigation prevent critical system errors. Electronic components can be affected by radiation, including naturally occurring background radiation. Due their reconfigurability, FPGAs exhibit not only with regard application data but also the configuration memory, which defines functionality of logic circuit. This paper proposes an...
Convolutional neural networks (CNNs) have been demonstrated to be a successful approach in the field of artificial intelligence (AI). Deploying CNNs on embedded devices at large scale would contribute significantly advancement and practical implementation AI various industries. However, complexity terms memory operation requirements poses challenges computing performance, bandwidth, flexibility executing hardware. This paper introduces framework that addresses these issues through model...