Efficient AI Backbones including GhostNet, TNT and MLP, developed by Huawei Noah's Ark Lab.
A curated collection of papers, benchmarks, surveys, and tools for model quantization, covering low-bit networks, LLMs, multimodal and generative models, vector and lattice quantization, and efficient deployment.
[ICML 2024] LLMCompiler: An LLM Compiler for Parallel Function Calling
EfficientFormerV2 [ICCV 2023] & EfficientFormer [NeurIPs 2022]
[CVPR 2024] DeepCache: Accelerating Diffusion Models for Free
Code for paper " AdderNet: Do We Really Need Multiplications in Deep Learning?"
List of papers related to neural network quantization in recent AI conferences and journals.
[NeurIPS 2024 Spotlight]"LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS", Zhiwen Fan, Kevin Wang, Kairun Wen, Zehao Zhu, Dejia Xu, Zhangyang Wang
[ICML 2024] SqueezeLLM: Dense-and-Sparse Quantization
Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.
Fast, lossless LLM inference via dual-view diffusion decoding.
[NeurIPS 2024] KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization
📚 Collection of awesome generation acceleration resources.
On-device LLM Inference Powered by X-Bit Quantization
Explorations into some recent techniques surrounding speculative decoding
[ECCV2022] Efficient Long-Range Attention Network for Image Super-resolution
[CVPR 2021] Exploring Sparsity in Image Super-Resolution for Efficient Inference
(CVPR 2021, Oral) Dynamic Slimmable Network
[NeurIPS 2024] AsyncDiff: Parallelizing Diffusion Models by Asynchronous Denoising
A curated list of papers and code on efficient diffusion models for image, video, world modeling, and language generation. Covering acceleration, quantization, compression, caching, and distillation.
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