Network Diagnosis Tools with industrial-grade quality are not widely available for common users such as researchers and students. This kind of tool enables users to develop Distributed Embedded Systems using low-cost and reliable setups. In the context of RISC-V Extensions and Domain-Specific Architecture, this paper proposes a Real-Time RISC-V-based CAN-FD Bus Diagnosis Tool, named RiscDiag CanFd, as an open-source alternative. The RISC-V Core extension is a CAN-FD Communication Unit controlled by a dedicated ISA Extension. Besides the extended RISC-V core, the proposed SoC provides UDP Communication via Ethernet for connecting the proposed solution to a PC. Additionally, a GUI application was developed for accessing and using the hardware solution deployed in an FPGA. The proposed solution is evaluated by measuring the lost frame rate, the precision of captured frames timestamps and the latency of preparing data for Ethernet communication. Measurements revealed a 0% frame loss rate, a timestamp error under 0.001% and an acquisition cycle jitter under 10 ns.
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http://dx.doi.org/10.3390/mi14010196 | DOI Listing |
Micromachines (Basel)
January 2023
Department of Computer Engineering, Faculty of Automatic Control and Computer Engineering, "Gheorghe Asachi" Technical University of Iași, 700050 Iași, Romania.
Network Diagnosis Tools with industrial-grade quality are not widely available for common users such as researchers and students. This kind of tool enables users to develop Distributed Embedded Systems using low-cost and reliable setups. In the context of RISC-V Extensions and Domain-Specific Architecture, this paper proposes a Real-Time RISC-V-based CAN-FD Bus Diagnosis Tool, named RiscDiag CanFd, as an open-source alternative.
View Article and Find Full Text PDFSensors (Basel)
February 2021
He-Arc Ingenierie, HES-SO, 2800 Delemont, Switzerland.
Standard-sized autonomous vehicles have rapidly improved thanks to the breakthroughs of deep learning. However, scaling autonomous driving to mini-vehicles poses several challenges due to their limited on-board storage and computing capabilities. Moreover, autonomous systems lack robustness when deployed in dynamic environments where the underlying distribution is different from the distribution learned during training.
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