H2PIPE: High Throughput CNN Inference on FPGAs with High-Bandwidth Memory
Bibliographic record
Abstract
Convolutional Neural Networks (CNNs) combine large amounts of parallelizable computation with frequent memory access. Field Programmable Gate Arrays (FPGAs) can achieve low latency and high throughput CNN inference by implementing dataflow accelerators that pipeline layer-specific hardware to implement an entire network. By implementing a different processing element for each CNN layer, these layer-pipelined accelerators can achieve high compute density, but having all layers processing in parallel requires high memory bandwidth. Traditionally this has been satisfied by storing all weights on chip, but this is infeasible for the largest CNNs, which are often those most in need of acceleration. In this work we augment a state-of-the-art dataflow accelerator (HPIPE) to leverage both High-Bandwidth Memory (HBM) and on-chip storage, enabling high performance layer-pipelined dataflow acceleration of large CNNs. Based on profiling results of HBM’s latency and throughput against expected address patterns, we develop an algorithm to choose which weight buffers should be moved off chip and how deep the on-chip FIFOs to HBM should be to minimize compute unit stalling. We integrate the new hardware generation within the HPIPE domain-specific CNN compiler and demonstrate good bandwidth efficiency against theoretical limits. Compared to the best prior work we obtain speed-ups of at least $19.4 \mathrm{x}, 5.1 \mathrm{x}$ and 10.5 x on ResNet-18, ResNet-50 and VGG-16 respectively.
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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 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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 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".