HiSpMM: High Performance High Bandwidth Sparse-Dense Matrix Multiplication on HBM-equipped FPGAs
Bibliographic record
Abstract
Sparse Matrix-Dense Matrix Multiplication (SpMM) is a critical operation in scientific computing, machine learning, and graph analytics. However, accelerating SpMM on FPGAs presents major challenges due to irregular memory access patterns and imbalanced workload distribution. In this work, we address a fundamental bottleneck in SpMM acceleration on High Bandwidth Memory (HBM)-equipped FPGAs: workload imbalance among processing elements (PEs). Additionally, we mitigate a scalability barrier present in state-of-the-art designs—namely, the tight coupling between PEs and HBM channels for dense matrix access. Furthermore, we provide an automated design space exploration framework. We propose HiSpMM, a high-performance SpMM accelerator architecture that introduces Dense Row Sharing to mitigate PE under-utilization by distributing heavy-row computations, a decoupled HBM access mechanism to allow independent scaling of PEs and memory bandwidth, and an automation tool that optimizes design parameters according to matrix structure-specific properties and user-defined hardware constraints. Our design achieves a geomean of \(5.81\times\) speedup and \(5.75\times\) energy efficiency improvement for imbalanced matrices compared to state-of-the-art designs, while also maintaining competitive performance for balanced matrices on the AMD/Xilinx U280 HBM FPGA board. Our HiSpMM project will be open sourced in the near future at https://github.com/SFU-HiAccel/HiSpMM .
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".