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Notes on pruning
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WIDE: Boosting Adaptive LLM Inference via Token-level Dynamic Width Pruning
Researchers introduce WIDE, a dynamic token-level pruning framework for improving the efficiency of large language models (LLMs). The framework allows each token to select attention and FFN channel groups dynamically…
Post-Training Pruning for Diffusion Transformers
The paper proposes DiT-Pruning, a method to improve post-training pruning performance for Diffusion Transformers (DiTs), which are used in image generation but have high computational overhead. The new method uses…
Hierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization
The authors present HiReLC, a framework that uses reinforcement learning to automatically compress deep neural networks by pruning and quantizing them. The framework works in two levels: low-level agents (LLAs)…
Hierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization
The paper presents HiReLC, a framework that uses reinforcement learning to compress deep neural networks by pruning and quantizing them. The framework divides the compression search into two levels: low-level agents…
Hierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization
The paper introduces HiReLC, a hierarchical reinforcement learning framework that automates the joint quantization and structured pruning of deep neural networks. It operates at two levels: low-level agents (LLAs)…
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