TL;DR
AutoSculpt introduces a pattern-based auto-pruning framework for DNNs that leverages graph learning and reinforcement learning to optimize model compression and runtime efficiency.
Contribution
It presents a novel pattern-based pruning method that automatically identifies and prunes regular structures in DNNs using graph encoding and DRL, improving efficiency and accuracy.
Findings
Achieves up to 90% pruning rates across various architectures.
Nearly 18% FLOPs reduction compared to baseline methods.
Outperforms existing auto-pruning approaches in accuracy and efficiency.
Abstract
As deep neural networks (DNNs) are increasingly deployed on edge devices, optimizing models for constrained computational resources is critical. Existing auto-pruning methods face challenges due to the diversity of DNN models, various operators (e.g., filters), and the difficulty in balancing pruning granularity with model accuracy. To address these limitations, we introduce AutoSculpt, a pattern-based automated pruning framework designed to enhance efficiency and accuracy by leveraging graph learning and deep reinforcement learning (DRL). AutoSculpt automatically identifies and prunes regular patterns within DNN architectures that can be recognized by existing inference engines, enabling runtime acceleration. Three key steps in AutoSculpt include: (1) Constructing DNNs as graphs to encode their topology and parameter dependencies, (2) embedding computationally efficient pruning…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
