Neuromorphic In-Memory RRAM NAND/NOR Circuit Performance Analysis in a CNN Training Framework on the Edge for Low Power IoT
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3219066
Abstract: Training a CNN involves computationally intense optimization algorithms to fit the network using a training dataset, to update the network weight for inferencing and then pattern classification. Hence, the application of in-memory computation would enable… read more here.
Keywords: memory; rram; cnn training; rram nand ... See more keywords
Accelerating CNN Training With Concurrent Execution of GPU and Processing-in-Memory
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Access"
DOI: 10.1109/access.2024.3488004
Abstract: Training of convolutional neural networks (CNN) consumes a lot of time and resources. While most previous works have focused on accelerating the convolutional (CONV) layer, the proportion of non-convolutional (non-CONV) layers, such as batch normalization,… read more here.
Keywords: memory; non conv; conv; cnn training ... See more keywords
FPDeep: Scalable Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Computers"
DOI: 10.1109/tc.2020.3000118
Abstract: Deep convolutional Neural Networks (CNNs) have revolutionized numerous applications, but the demand for ever more performance remains unabated. Scaling CNN computations to larger clusters is generally done by distributing tasks in batch mode using methods… read more here.
Keywords: cnn training; acceleration cnn; scalable acceleration; fpdeep scalable ... See more keywords
WinTA: An Efficient Reconfigurable CNN Training Accelerator With Decomposition Winograd
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Circuits and Systems I: Regular Papers"
DOI: 10.1109/tcsi.2023.3338471
Abstract: Convolutional neural networks (CNNs) are expected to bridge the domain shift between the training data and real-world tasks. Moreover, the efficient training of CNNs on resource-constrained platforms has become more important because of communication latency… read more here.
Keywords: reconfigurable cnn; training; cnn training; decomposition winograd ... See more keywords
Calibration of Compact Resist Model Through CNN Training
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Semiconductor Manufacturing"
DOI: 10.1109/tsm.2023.3267670
Abstract: We notice that the compact resist model can be mapped to a simple CNN (convolutional neural network): convolutional layer corresponds to convolutions between input images and resist kernels, and a fully connected layer can model… read more here.
Keywords: compact resist; resist model; cnn training; model ... See more keywords
Acceleration of Deep Neural Network Training Using Field Programmable Gate Arrays
Sign Up to like & getrecommendations! Published in 2022 at "Computational Intelligence and Neuroscience"
DOI: 10.1155/2022/8387364
Abstract: Convolutional neural network (CNN) training often necessitates a considerable amount of computational resources. In recent years, several studies have proposed for CNN inference and training accelerators in which the FPGAs have previously demonstrated good performance… read more here.
Keywords: neural network; network; acceleration deep; deep neural ... See more keywords