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Development of a generalized model for parallel-streaming neural element and structures for scalar product calculation devices

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Abstract

Nowadays, intensive streams of fuzzy input data need to be processed in real-time for different fields of science and engineering. To solve this problem, a generalized model for the parallel-streaming neural element was developed in this paper. The proposed model allows minimizing hardware costs while providing scalar product and activation function calculations in real time. In particular, an algorithm and a structure for a parallel-streaming device (PSD) were developed to calculate a scalar product with the direct formation of partial products based on the analysis of a single bit-cut of multipliers, which provides working with the shortest conveyor stage. It is based on a modified Booth’s algorithm that allows reducing equipment costs for processing operands with high bit-width. Moreover, it promotes the lowest equipment costs for the operands with a low bit-width. Besides, researches demonstrate that the main way of increasing the speed of the developed algorithms and structures of PSD for scalar product calculating is a preliminary formation of partial products. Further, the estimation of the model parameters shows reducing conveyor steps, improvement of the locality of connections, and an increase of an adaptation to the coming data intensity. It is proposed to use the developed algorithms and structures as a basis for building devices for parallel-streaming calculation of the scalar product in real time with high efficiency of equipment use. The main ways of harmonizing the time of incoming data and weights with the conveyor cycle of the PSD for calculation of the scalar product are determined. A methodology proposed for building conveyor devices for parallel-streaming calculation of the scalar product in real time for a given intensity of input data ensures the implementation of devices with the required speed and with minimal hardware costs.

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Authors and Affiliations

  1. Department of Automated Control Systems, Lviv Polytechnic National University, Lviv, Ukraine

    Ivan Tsmots, Vasyl Teslyuk, Oleksa Skorokhoda & Iryna Kazymyra

  2. Department of Information Systems, Faculty of Management, Comenius University in Bratislava, Bratislava, Slovakia

    Natalia Kryvinska

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  1. Ivan Tsmots
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Correspondence to Natalia Kryvinska.

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Tsmots, I., Teslyuk, V., Kryvinska, N. et al. Development of a generalized model for parallel-streaming neural element and structures for scalar product calculation devices. J Supercomput 79, 4820–4846 (2023). https://doi.org/10.1007/s11227-022-04838-0

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