Paper Title
Neuronlink: An Effective Large Scale Neural Network Accelerators Based on Chip-To-Chip Interconnect
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Registration ID: IJNRD_181217
Published ID: IJNRDA001063
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Keywords
Neural Network Accelerator, Deep Neural Network, Nodes
Abstract
To control strong neuron-to-neuron traffic, Many processing nodes form a high-level neural network (NN), which can be used as a spinal or multi-core tool and built using network-on-chip (NoC). Chip-to-chip communication networks bring together many NN-based processors based on NoC to improve complete neural acceleration. A large amount of multicast-based traffic passes through a chip or between chips, making it difficult to network communication design and NN system performance and power impact. We recommend NeuronLink for NN accelerators in this study, which combines intrachip and interchip communication methods. With virtual-channel intrachip communication, we provide cross-cutting solution, troubleshooting, and compatible route calculation methods, resulting in the best NoC with reduced hardware cost based on multiple streams. For interchip communications, we propose a chip-to-chip communication system with NoC-aware that allows for efficient communication of NN-based NN devices. We have also put the proposed methods to the test on DNN-based NoC chips with four flexible gate frames (FPGAs). Test results show that the proposed communication network can better manage data flow within DNNs with higher output and less overhead compared to modern connections.
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How To Cite (APA)
Brindha J, Thathiksha S, Swetha S, Nanditha K, & SurthiS (May-2022). Neuronlink: An Effective Large Scale Neural Network Accelerators Based on Chip-To-Chip Interconnect . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 7(5), 348-352. https://ijnrd.org/papers/IJNRDA001063.pdf
Issue
Volume 7 Issue 5, May-2022
Pages : 348-352
Other Publication Details
Paper Reg. ID: IJNRD_181217
Published Paper Id: IJNRDA001063
Research Area: Engineering
Author Type: Indian Author
Country: TIRUVALLUR, TAMIL NADU, India
Published Paper PDF: https://ijnrd.org/papers/IJNRDA001063.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRDA001063
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