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Model compression for deep neural networks: A survey
Currently, with the rapid development of deep learning, deep neural networks (DNNs) have
been widely applied in various computer vision tasks. However, in the pursuit of …
been widely applied in various computer vision tasks. However, in the pursuit of …
Edge-cloud polarization and collaboration: A comprehensive survey for ai
Influenced by the great success of deep learning via cloud computing and the rapid
development of edge chips, research in artificial intelligence (AI) has shifted to both of the …
development of edge chips, research in artificial intelligence (AI) has shifted to both of the …
H2o: Heavy-hitter oracle for efficient generative inference of large language models
Abstract Large Language Models (LLMs), despite their recent impressive accomplishments,
are notably cost-prohibitive to deploy, particularly for applications involving long-content …
are notably cost-prohibitive to deploy, particularly for applications involving long-content …
Deja vu: Contextual sparsity for efficient llms at inference time
Large language models (LLMs) with hundreds of billions of parameters have sparked a new
wave of exciting AI applications. However, they are computationally expensive at inference …
wave of exciting AI applications. However, they are computationally expensive at inference …
Smoothquant: Accurate and efficient post-training quantization for large language models
G ** attention heads do nothing
Transformer models have been widely adopted in various domains over the last years and
especially large language models have advanced the field of AI significantly. Due to their …
especially large language models have advanced the field of AI significantly. Due to their …
A survey of quantization methods for efficient neural network inference
This chapter provides approaches to the problem of quantizing the numerical values in deep
Neural Network computations, covering the advantages/disadvantages of current methods …
Neural Network computations, covering the advantages/disadvantages of current methods …
Post-training quantization for vision transformer
Recently, transformer has achieved remarkable performance on a variety of computer vision
applications. Compared with mainstream convolutional neural networks, vision transformers …
applications. Compared with mainstream convolutional neural networks, vision transformers …