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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 …
Gptq: Accurate post-training quantization for generative pre-trained transformers
E Frantar, S Ashkboos, T Hoefler, D Alistarh - ar** attention heads do nothing
Y Bondarenko, M Nagel… - Advances in Neural …, 2023 - proceedings.neurips.cc
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 …
Hardware-aware training for large-scale and diverse deep learning inference workloads using in-memory computing-based accelerators
Analog in-memory computing—a promising approach for energy-efficient acceleration of
deep learning workloads—computes matrix-vector multiplications but only approximately …
deep learning workloads—computes matrix-vector multiplications but only approximately …