Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Deflection-routed NoCs like Hoplite and HopliteRT take advantage of FPGA-specific features to deliver low-cost, high-frequency, FPGA-friendly communication networks. However, they suffer from long packet deflection penalties, low sustained throughputs, and feature limitations such as out-of-order delivery of packets. In this paper, we introduce the HopliteBuf NoC, and an associated static analysis tool, that eliminates deflections entirely while simultaneously adding in-order delivery feature using (1) small, stall-free FIFOs with provable occupancy bounds, and (2) linearization of vertical rings of the torus Hoplite topology to improve provable link utilization. We implement these FIFOs using cheap LUT SRAMs (Xilinx SRL32s, and Intel MLABs) to absorb packet contention. We evaluate conditions for stall-free behavior using static analysis that compute upper bounds on FIFO occupancy based on the communication pattern. Our static analysis deliver bounds that are not only better (in latency) than HopliteRT but also tighter by 2--3×. Across 100 randomly-generated flowsets mapped to a 5×5 system size, HopliteBuf is able to route a larger fraction of these flowsets with \textless 128-deep FIFOs, boost worst-case routing latency by $\approx$2× for mutually feasible flowsets. At 20% injection rates, HopliteRT is only able to route 1--2% of the flowsets while HopliteBuf can deliver 40--50% sustainability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it