Mr. Sergei Tabunschik Profile

Sergei Tabunschik

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Proceedings Article | 27 November 2023 Poster + Paper

Neural processor architecture design for CNN processing acceleration

Sergei Tabunschik, Sergei Bykovskii, Alexander Antonov, Alexander Belozubov, Pavel Kustarev, Andrei Zhdanov

Proceedings Volume 12767, 127671G (2023) https://doi.org/10.1117/12.2687678

KEYWORDS: Mixed reality, Neural networks, Portability, Design and modelling, Power consumption, Computer architecture, Visualization, Convolutional neural networks, Wearable devices, Virtual reality

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The current work introduces a neural processor architecture that allows hardware acceleration in the processing of convolutional neural networks (CNNs). The purpose of this study is to design the architecture and microarchitecture of the neural processor, which can be used to solve the problems of environmental analysis and recognition of objects that are located in the scene in augmented reality systems, presented as energy-efficient and compact wearable devices. The proposed architecture provides the opportunity to adjust the variable parameters of blocks for data processing and storage to optimize the performance, energy consumption and resources used to implement the neural processor. The article offers variants of scaling of computing blocks and memory blocks in the architecture of a neural processor, which can be used for increasing the performance of the end product. The paper describes a tool that generates a neural processor based on given limitations on power consumption and performance and on the structure of convolutional neural networks that you need to use for data processing. The proposed tool can potentially become a valuable product in the field of designing hardware accelerators of convolutional neural networks, as it allows to increase the degree of automation of the process of synthesis of neural processors for further implementation in mixed reality systems, made as portable devices. In general, this thesis presents tools that allow the developer of software based on convolutional neural networks for mixed reality systems to synthesize energy-efficient processors to accelerate the processing of convolutional neural networks.

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