A new technical paper titled “APOSTLE: Asynchronously Parallel Optimization for Sizing Analog Transistors Using DNN Learning” was published by researchers at UT Austin and Analog Devices. “Analog ...
Abstract: Communication delays and synchronization are major bottlenecks for parallel computing, and tolerating asynchrony is therefore crucial for accelerating parallel computation. Motivated by ...
Abstract: Gannet optimization algorithm (GOA) is a meta-heuristic algorithm based on habits of gannet proposed by Zhang et al. In this paper, we propose a Gannet optimization algorithm using parallel ...
As an adaptive learning rate stochastic optimization method, Adam has been widely used in the field of deep learning since it was first proposed in 2014. In order to improve its training efficiency ...
A Distributed Parallel Training Simulation Tool (AdpartSim) for Data Center focuses on helping us study and simulate the parallel optimization strategies of Large Models (LM), as well as the impact of ...
Status: This repository is still under development, expecting new features/papers and a complete tutorial to explain it. Feel free to raise questions/suggestions through GitHub Issues, if you want to ...
Hyper-parameters are parameters used to regulate how the algorithm behaves while it creates the model. These factors cannot be discovered by routine training. Before the model is trained, it must be ...
1 Institute of Electronic and Electrical Engineering, Civil Aviation Flight University of China, Guanghan, China 2 School of Information Engineering, Southwest University of Science and Technology, ...
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