Zafer Attal, M.Sc.

Sporadic anomalies in automotive systems can degrade performance over time and may originate from various system components. In automotive applications, anomalies are often observed at the sensor and ECU levels, with potential propagation through the in-vehicle network via Ethernet. Such anomalies may be the result of deviations in electronic control units, highlighting the importance of monitoring these signals over Ethernet.

Not all processing anomalies are equally detectable over Ethernet due to inherent limitations in the monitoring techniques and the nature of the anomalies. This seminar will explore various anomaly categories, investigate their potential causes, and assess the likelihood of their propagation through the network.

The goal of this seminar is to provide a comprehensive analysis of these anomaly categories, evaluate the underlying causes, and discuss the potential for their detection and mitigation when monitored over Ethernet.

In today’s data-driven world, processing approaches are typically divided between cloud-based solutions—with virtually unlimited resources—and localized processing, which is constrained by hardware limitations. While the cloud offers extensive computational power, localized processing is often required for real-time applications where latency and data security are critical concerns.

To bridge this gap, various algorithms have been developed to pre-process data or extract essential information before it is sent to the cloud.

The goal of this seminar is to explore and compare these algorithms, evaluating their computational load on local hardware and their overall impact on system performance.

Modern CPUs execute a vast number of instructions while managing large volumes of data. On-chip debugging modules, located adjacent to the CPU, play a critical role in capturing valuable execution information. This data is essential for analyzing system behavior and detecting anomalies—such as timing issues or execution faults—that may occur in the processing unit.

Over time, various algorithms have been developed to analyze processor traces and instructions. These algorithms not only deepen our understanding of system behavior but also support the debugging of potential faults and anomalies.

The goal of this seminar is to explore and compare different trace analysis algorithms, and that is by evaluating their efficiency, performance, and potential applications in debugging and optimizing processor operations.

About the Project:
Future cars rely on a wide variety of sensors—including cameras, LiDARs, and RADARs—that generate enormous amounts of data. This data flows through the intra-vehicular network (IVN) to processing nodes, ultimately triggering actuators. With strict timing constraints essential for vehicle safety, time-sensitive networking (TSN) is now a critical component in modern automotive systems. Within the context of the EMDRIVE project, our team is developing new monitoring and diagnostic approaches to detect errors early and maintain functional safety in highly automated driving environments.

Project Description:
The primary goal of this project is to migrate existing software packages—used to record ECU traces and analyze processing anomalies—onto the ZCU102 board. This migration will enable local processing of anomalies and establish a robust PS/PL interface between the anomaly detection hardware (implemented on the FPGA) and the processing system running the software.

The key tasks include:

• TAS Tool Configuration: Bring up the TAS tool and configure it to work with the Multi Core Debug Solution (MCDS) for trace recording.

• Trace Analyzer Deployment: Bring up and configure the Trace Analyzer to parse recorded traces and detect deviations in processing.

• Software Migration: Migrate the existing software packages to run on the Processing System (PS) of the ZCU102 board.

• Interface Integration: Develop and integrate a stable interface between the Programmable Logic (PL) and the PS, ensuring efficient sharing of data, status, and configuration information.

Key Responsibilities:

• Analyze existing software packages and understand the hardware integration requirements.
• Configure and validate both the TAS tool and the Trace Analyzer.
• Adapt and optimize software for deployment on the ZCU102 board.
• Develop and implement a robust PS/PL interface for seamless communication between hardware and software.
• Collaborate with interdisciplinary teams to integrate and test the complete system.

Voraussetzungen