Demonstration of an FPGA controller for guaranteed-rate optical packet switching
Bibliographic record
Abstract
A switch controller which achieves low-latency deterministic `Guaranteed-Rate' (GR) connections in packet switched networks is demonstrated on a `Field Programmable Gate Array' (FPGA) device. A Software Defined Networking (SDN) control plane can configure the FPGA controllers to establish deterministic GR connections in a forwarding plane of IP routers or layer-2 packet switches. The use of deterministic GR connections can reduce queueing delays to negligible values, so that the end-to-end delays are reduced to the fiber latency. The switch controller can operate routers, switches and links at 100% loads, while simultaneously guaranteeing very low end-to-end queueing delays. A testbed consisting of 8 switches in a linear array is synthesized on an Altera Cyclone IV FPGA. An SDN control plane routes 128 traffic flows through the testbed to saturate the switches and links. Packets move through the forwarding plane at a clock rate of 52 MHz, transferring millions of packets per second, and statistics are recorded. The demonstration shows that all traffic flows in the testbed achieve deterministic GR service. The FPGA demonstration is accompanied by a video which visually illustrates packet forwarding in the network. The GR technology applies to IP routers and layer 2 packet switched networks, with thousands of nodes spanning arbitrary distances. The FPGA controller also applies to layer 2 MPLS (Multiprotocol Label Switching) and Ethernet switches, and to `Optical Packet Switched' networks, to achieve deterministic GR services and to operate links at 100% loads.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".