Neural Network Model Dynamic Shape

This perspective article delves into the transformative realm of dynamic neural networks, which is reshaping AI with adaptable structures and improved effi

Understanding Tensor Shapes In TensorFlow, every tensor is described by its shape. The shape of a tensor provides information about its dimensions, which is critical for understanding how data flows through a neural network. Static Shape Defined at graph construction time and does not change.

Nimble Efficiently Compiling Dynamic Neural Networks for Model Inference Haichen Shen, Jared Roesch, Zhi Chen, Wei Chen, Yong Wu, Mu Li, Vin Sharma, Zachary Tatlock, Yida Wang

Optimizing dynamic neural networks is more challenging than static neural networks optimizations must consider all possible execution paths and tensor shapes. This paper proposes Nimble, a high-performance and flexible system to optimize, compile, and execute dynamic neural networks on multiple platforms.

Dynamic Neural Networks. The main objective of DyNNs is to decrease the energy consumption of the model inference for inputs that can be classified with fewer compu-tational resources.

Since I am trying to make a Fully Convolutional Neural Network which converts grayscale images to rgb images, I was wondering if I could train and test the model on different sized images different pixels and ratio.

Dynamic Neural Networks DNNs are an evolving research field within deep learning DL, offering a robust, adaptable, and efficient alternative to the conventional Static Neural Networks SNNs. Unlike SNNs, which maintain a fixed architecture of layers, nodes, and connections throughout their operation, DNNs introduce flexibility by allowing modifications to their structure during inference

Modern deep neural networks increasingly make use of features such as dynamic control flow, data structures and dynamic tensor shapes. Existing deep learning systems focus on optimizing and executing static neural networks which assume a pre-determined model architecture and input data shapes--assumptions which are violated by dynamic neural networks. Therefore, executing dynamic models with

To address the need for effective and efficient optimization of dynamic-shape neural networks, this paper introduces MikPoly, a novel dynamic-shape tensor compiler based on micro-kernel polymerization.

A computer-implemented method for compiling a neural network with tensors having dynamic shapes includes parsing the neural network using a set of global virtual dimension identifications IDs that define the dynamic shapes of one or more of the tensors of the neural network. The method further includes performing shape checks while building a computation graph using the set of global virtual