We propose a quantized tensor neural network in this article (QTNN), which integrates the advantages of neural networks and tensor networks.
Feb 9, 2022 · The main ideal of TNN is to extend the neural network models to a more general high-dimensional situation through the tensor decomposition model ...
3 days ago · Only the weights of the trained model are quantized. Weights can be quantized at different granularity levels (per layer, per tensor, etc.).
We establish a training framework for three-dimensional convolutional neural networks (3DCNNs) named QTTNet that combines tensor train (TT) decomposition and ...
Missing: Network. | Show results with:Network.
People also ask
What is a quantized neural network?
What is a quantum tensor network?
What are tensors in a neural network?
Why might you quantize a neural network model for deployment on an MCU?
Aug 3, 2022 · We implement Tensor Neural Networks (TNN), a quantum-inspired neural network architecture that leverages Tensor Network ideas to improve upon deep learning ...
Nov 14, 2024 · We demonstrated quantization on a tensor and a neural network, noting that the quantized model had a significantly smaller memory footprint ...
Quantization is a technique to reduce the computational and memory costs of running inference by representing the weights and activations with low-precision ...
Oct 5, 2021 · What is neural network quantization? For any given trained neural network: • Store weights in low bits (INT8). • Compute calculations in ...
To address this problem, we establish a training framework for three-dimensional convolutional neural networks (3DCNNs) named QTTNet that combines tensor train ...
In this work, using a method based on tensor decomposition, network parameters were compressed, thereby reducing access to external memory.