An FPGA controller for deterministic guaranteed-rate optical packet switching
Bibliographic record
Abstract
An FPGA controller which can establish low-latency deterministic `Guaranteed-Rate' (GR) transport connections for cloud services is explored. An SDN control-plane can create deterministic GR connections in a forwarding-plane of electrical or optical packet switches, where end-to-end delays are reduced to the fiber latency. A testbed for the proposed deterministic GR technology is presented. A forwarding-plane consisting of 8 controllers and minimum-complexity packet-switches are synthesized on an Altera Cyclone IV FPGA. An SDN control plane routes 128 traffic flows through the forwarding plane, which saturate the packet-switches. Several deterministic schedules are precomputed and loaded into the controller lookup tables, which control each packet-switch for each time-slot in a scheduling frame. The FPGA testbed is clocked at 52 MHz, allowing millions of packet transfers per second, and statistics are recorded. The testbed confirms the establishment of deterministic GR transport connections configured on a programmable underlay network, where the end-to-end delays are effectively reduced to the fiber latency. An inexpensive FPGA controller can add deterministic GR services to IP routers, MPLS, Ethernet, InfiniBand and Fibre channel switches, and layer-2 electrical or optical packet-switches, spanning 100s of nodes over distances of 1000s of miles. By using existing Silicon-Photonics technology, an SDN control-plane can manage deterministic GR connections in a programmable underlay network of integrated single-chip Optoelectronic Packet Switches, with aggregate capacities in the 100s of Terabits/sec.
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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) | 0.008 | 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".