SDA: Low-Bit Stable Diffusion Acceleration on Edge FPGAs
Bibliographic record
Abstract
This paper introduces SDA, the first effort to adapt the expensive stable diffusion (SD) model for edge FPGA deployment. First, we apply quantization-aware training to quantize its weights to 4 -bit and activations to 8 -bit ($W 4 A 8$) with a negligible accuracy loss. Based on that, we propose a high-performance hybrid systolic array (hybridSA) architecture that natively executes convolution and attention operators across varying quantization bit-widths (e.g., $W 4 A 8$ and all 8 -bit $Q K^{T} V$ in attention). To improve computational efficiency, hybridSA integrates diverse DSP packing techniques into hybrid weightstationary and output-stationary dataflows that are optimized for convolution and attention. It also supports flexible dataflow transitions to address the distinct demands of its output sequence by subsequent nonlinear operators. Moreover, we observe that nonlinear operators become the new performance bottleneck after the acceleration of convolution and attention, and offload them onto the FPGA as well. To reduce the latency of each nonlinear operator, we pipeline its own execution at a fine granularity. To minimize the resource utilization of nonlinear operators, we carefully balance their execution with hybridSA in a coarse-grained pipeline. Experimental results demonstrate that our low-bit ($W 4 A 8$) SDA accelerator on the embedded AMDXilinx ZCU102 FPGA achieves a speedup of $97.3 \times$ (which takes about $\mathrm{2 . 1}$ minutes for one SD inference), compared to the original SD-v1.5 model on the ARM Cortex-A53 CPU (which takes about 3.5 hours for one SD inference). Our SDA project is open sourced here: https://github.com/Michaela1224/SDA_code.
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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) | 0.009 | 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".