AI & ML
An Experimental Evaluation of Scheduled Transmission and Hardware Timestamping on the Intel E830 NIC
arXiv cs.NI0 views
arXiv:2610.11589v1 Announce Type: new Abstract: High-precision packet transmission is increasingly important in deterministic networking applications, including 5G fronthaul and Time-Sensitive Networking (TSN). Some recent high-speed network interface cards (NICs), including the Intel E830 and NVIDIA ConnectX series, support hardware-assisted transmission at specified times and hardware timestamping of received and transmitted frames. Despite the relevance of these capabilities to the stringent timing requirements of 5G fronthaul, little public information is available on their timing accuracy and performance characteristics. As a companion to our previous study of scheduled transmission on the NVIDIA ConnectX series, this paper experimentally characterizes the scheduled transmission and hardware timestamping capabilities of the Intel E830 NIC. Using an FPGA-based measurement platform with deterministic frame generation and nanosecond-resolution timestamping, we evaluate receive and transmit hardware timestamping and scheduled-transmission accuracy. The results show only several-nanosecond timing variation in hardware timestamping. Receive timestamp-derived frame intervals showed a peak-to-peak variation of up to 6 ns, while the transmit timestamp comparison showed a residual range of 4.6 ns after clock-drift compensation. Approximately 99% of the observed frame intervals were within $\pm$300 ns of the target interval, with occasional outliers of up to approximately 2 us. These results indicate that scheduled transmission can support applications with timing requirements on the order of several tens of microseconds, such as 5G fronthaul, but may be insufficient for TSN applications requiring transmission timing accuracy on the order of a few to a few tens of nanoseconds.
Source:https://arxiv.org/abs/2610.11589PreviousEnergy-Aware Receiver-initiated MAC Protocol for Energy Harvesting IoTNextDigital Twin for Pre-Deployment Validation of AI-Driven Safety-Critical Industrial Edge Control Loops
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