Managing HBM Bandwidth on Multi-Die FPGAs with FPGA Overlay NoCs
Bibliographic record
Abstract
We can improve HBM bandwidth distribution and utilization on a multi-die FPGA like Xilinx Alveo U280 by using Overlay Network-on-Chips (NoCs). The HBM in Xilinx Alveo U280 offers 8 GB of memory capacity with a theoretical maximum bandwidth of 460 GBps, but exposed all the HBM ports to the FPGA fabric in only one die. As a result, computing elements assigned to other dies must use the scarce Super Long Lines (SLLs) to access HBM bandwidth. Furthermore, HBM is fractured internally into thirty-two smaller memories called pseudo channels, connected together by a hardened and performance-limited crossbar. The crossbar enables global accesses from any of the HBM ports, but introduces several throughput bottlenecks. An Overlay Hybrid NoC combining Hoplite NoC with Butterfly Fat Trees (BFT) NoCs offers a high-performance solution for distributing HBM bandwidth across all three dies. The routing capability of the NoC can be modified to supplant the internal crossbar of Xilinx HBM for global accesses. We demonstrate this in Xilinx Alveo U280 with BFT, Hoplite, and Hybrid NoC, using synthetic benchmarks and two application-based benchmarks, Dense matrix-matrix multiplication (DMM) and Sparse Matrix-Vector multiplication (SPMV). Our experiments show that Overlay NoCs can improve the throughput by 1.26× for synthetic benchmarks and up to 1.4× for SpMV workloads.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".