[Paper] PrimeSVT: An Automated Memory-aware Pruning Framework with Prioritized Compression Policy for Spiking Vision Transformers

Source: arXiv - 2606.03428v1

Overview

Spiking Vision Transformers (SViTs) promise ultra‑low‑power visual processing for edge devices, but their massive parameter counts make on‑chip deployment impractical. The new PrimeSVT framework automates memory‑aware, structured pruning of pre‑trained SViTs, delivering sizable memory savings while keeping accuracy within a few percent—without the need for custom sparsity‑aware hardware or tedious manual tuning.

Key Contributions

• Automated, memory‑constrained pruning: A single‑shot pipeline that respects user‑specified memory budgets and accuracy tolerances.
• Prioritized compression policy: Layers are ranked by size and pruned sequentially from largest to smallest, exploiting each layer’s robustness to pruning.
• Structured, channel‑wise filter pruning: Uses L2‑norm ranking to remove whole filters, yielding hardware‑friendly sparsity (no irregular patterns).
• Design‑time reduction: Eliminates the manual trial‑and‑error process traditionally required to pick pruning rates for each layer.
• Empirical validation on SViTs: Demonstrates up to 26.7 % memory reduction with ≤ 3 % top‑1 accuracy loss (70.3 % without fine‑tuning, 72.9 % after fine‑tuning vs. 73.3 % baseline).

Methodology

1. Layer ranking – The framework first measures the number of parameters per transformer block and sorts layers from biggest to smallest.
2. Robustness profiling – For each layer, a quick sensitivity analysis determines how much pruning it can tolerate before accuracy degrades beyond the user‑defined threshold.
3. Prioritized pruning loop – Starting with the largest layer, PrimeSVT applies channel‑wise filter pruning: filters (i.e., entire attention heads or MLP channels) are scored by their L2‑norm; the lowest‑scoring filters are dropped.
4. Constraint checking – After each layer’s pruning step, the framework checks whether the cumulative memory saving meets the target and whether the projected accuracy stays within the allowed drop. If not, it backs off to a milder pruning rate for that layer.
5. Optional fine‑tuning – A lightweight fine‑tuning pass (few epochs) can be run to recover any remaining accuracy loss.

All steps are fully automated, requiring only the original pretrained SViT model and two numbers from the user: maximum allowable memory reduction and maximum accuracy drop.

Results & Findings

Key take‑aways:

• Structured pruning preserves regular memory layout, enabling immediate speed‑ups on existing hardware.
• The prioritized policy yields better memory‑accuracy trade‑offs than naïve uniform pruning across layers.
• A single fine‑tuning pass recovers almost all lost accuracy, confirming that the pruning decisions are not overly aggressive.

Practical Implications

• Edge AI developers can now compress SViTs to fit within the tight RAM budgets of microcontrollers or low‑power SoCs without rewriting kernels or designing custom accelerators.
• Model‑as‑a‑service pipelines can integrate PrimeSVT as an automated post‑processing step, turning any pretrained SViT into an “embed‑ready” artifact with a single command.
• Hardware vendors benefit because the resulting models use dense matrix operations; existing BLAS‑optimized libraries can be leveraged, avoiding the need for sparse‑matrix support.
• Rapid prototyping: Teams no longer need to manually experiment with dozens of pruning ratios per layer—PrimeSVT’s sensitivity analysis does the heavy lifting, cutting design cycles from weeks to hours.

Limitations & Future Work

• The current sensitivity analysis is performed on a validation subset; extreme domain shifts could misestimate robustness, leading to sub‑optimal pruning.
• Fine‑tuning is still required for the best accuracy, albeit briefly; fully “zero‑training” compression remains an open challenge.
• The framework focuses on memory reduction; latency or energy‑aware pruning (e.g., targeting specific hardware pipelines) is not explicitly modeled.
• Extending the approach to other spiking neural network families (e.g., spiking CNNs) and exploring joint quantization‑pruning strategies are promising next steps.

Authors

• Rachmad Vidya Wicaksana Putra
• Achyuta Muthuvelan
• Alberto Marchisio
• Muhammad Shafique

Paper Information

• arXiv ID: 2606.03428v1
• Categories: cs.NE, cs.AI, cs.LG
• Published: June 2, 2026
• PDF: Download PDF