arXiv:2605.24754v1 Announce Type: cross Abstract: Neural network weights are increasingly a bottleneck for deployment, yet most compression pipelines treat layers independently and overlook cross-layer redundancy induced by function-preserving symmetries. We propose Motion-Compensated Weight Compression (MCWC), a weight-only codec that aligns permutation-symmetric blocks (e.g., hidden units and attention heads) to maximize cross-layer correspondence, turning depth into a predictable sequence. In the aligned coordinate system, MCWC uses a lightweight layer-sequential predictor with periodic key
The increasing size of neural networks presents a critical deployment bottleneck, necessitating innovative compression techniques to improve efficiency without sacrificing performance.
This research offers a novel approach to weight compression that addresses cross-layer redundancies, potentially making large AI models more manageable and deployable in constrained environments.
Current compression methods primarily focus on individual layers; this new technique considers the entire network architecture's symmetries to achieve more efficient compression.
- · AI model developers
- · Edge AI computing
- · Cloud service providers
- · Hardware manufacturers
- · Inefficient AI deployment methods
Reduced computational and memory requirements for deploying large neural networks.
Accelerated adoption of more complex AI models in resource-limited applications and devices.
Potentially democratizes access to advanced AI capabilities by lowering the barrier to deployment for smaller entities.
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Read at arXiv cs.LG