A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
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Lazydit introduces a lazy learning approach to accelerate Diffusion Transformers by selectively skipping redundant computations during inference.
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This survey provides a comprehensive overview of efficient diffusion models, covering acceleration techniques, architectural optimizations, and specialized frameworks.
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Proposes Dynamic Sparse Attention (DSA) to efficiently exploit dynamic sparse patterns in attention for scalable transformer acceleration.
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FastKV decouples context reduction from KV cache compression to accelerate both prefill and decoding phases in LLM inference.
D. Du, Gu Gong, Xiaowen Chu
A comprehensive survey of model quantization and hardware acceleration techniques for vision transformers.
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This survey comprehensively reviews knowledge distillation techniques for model compression and acceleration.
Hongzheng Chen, Jiahao Zhang, Yixiao Du, et al.
This paper investigates FPGA-based spatial acceleration for LLM inference, achieving up to 13.4x speedup over prior FPGA accelerators and 5.7x energy efficiency vs. A100 GPU.