Associative Co-processor on the Basis of Programmable Logical Integrated Circuits for Special Purpose Computer Systems

Martyshkin, Alexey I.; Сальников, И. И.; Pashchenko, Dmitry V.; Trokoz, Dmitry A.

doi:10.1109/glosic.2018.8570067

Cited by 9 publications

(7 citation statements)

References 8 publications

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“…People solve the segmentation problem easily, but it is difficult for a computer program. When the image is segmented, the program must classify each individual digit (Mohamad, 2003;Martyshkin & Martens-Atyushev, 2019;Martyshkin et al, 2018). The segmentation problem is not difficult to solve if there is a good classifier of individual numbers.…”

Section: Methodsmentioning

confidence: 99%

Neural Network for Handwriting Recognition

Butaev¹,

Babich²,

Salnikovq

et al. 2020

Nexo Revista Científica

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Today, in the digital age, the problem of pattern recognition is very relevant. In particular, the task of text recognition is important in banking, for the automatic reading of documents and their control; in video control systems, for example, to identify the license plate of a car that violated traffic rules; in security systems, for example, to check banknotes at an ATM and in many other areas. A large number of methods are known for solving the problem of pattern recognition, but the main advantage of neural networks over other methods is their learning ability. It is this feature that makes neural networks attractive to study. The article proposes a basic neural network model. The main algorithms are considered and a programming model is implemented in the Python programming language. In the course of research, the following shortcomings of the basic model were revealed: low learning rate (the number of correctly recognized digits in the first epochs of learning); retraining - the network has not learned to generalize the knowledge gained; low probability of recognition - 95.13%.To solve the above disadvantages, various techniques were used that increase the accuracy and speed of work, as well as reduce the effect of network retraining.

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Section: Methodsmentioning

confidence: 99%

Neural Network for Handwriting Recognition

Butaev¹,

Babich²,

Salnikovq

et al. 2020

Nexo Revista Científica

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“…Through associative co-processors, there is always a chance to have special-purpose computers run heavy programs, as [11] explained. Reference [11] proposes using current hardware components to construct a multi-core processor for particularly unique computer systems.…”

Section: Related Workmentioning

confidence: 99%

Shared Cache Based on Content Addressable Memory in a Multi-Core Architecture

Abumwais¹,

Obaid²

2023

Computers, Materials &Amp; Continua

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Modern shared-memory multi-core processors typically have shared Level 2 (L2) or Level 3 (L3) caches. Cache bottlenecks and replacement strategies are the main problems of such architectures, where multiple cores try to access the shared cache simultaneously. The main problem in improving memory performance is the shared cache architecture and cache replacement. This paper documents the implementation of a Dual-Port Content Addressable Memory (DPCAM) and a modified Near-Far Access Replacement Algorithm (NFRA), which was previously proposed as a shared L2 cache layer in a multi-core processor. Standard Performance Evaluation Corporation (SPEC) Central Processing Unit (CPU) 2006 benchmark workloads are used to evaluate the benefit of the shared L2 cache layer. Results show improved performance of the multicore processor's DPCAM and NFRA algorithms, corresponding to a higher number of concurrent accesses to shared memory. The new architecture significantly increases system throughput and records performance improvements of up to 8.7% on various types of SPEC 2006 benchmarks. The miss rate is also improved by about 13%, with some exceptions in the sphinx3 and bzip2 benchmarks. These results could open a new window for solving the long-standing problems with shared cache in multi-core processors.

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“…After performing some action, the system changes states. The state of the system is only able to change thus when a certain event occurs (Zimina, 2017;Martyshkin et al, 2018).…”

Section: Simulation Modellingmentioning

confidence: 99%

Research of a multithreaded non-deterministic system model

Pashchenko

Trokoz

Martyshkin

et al. 2021

Nexo Revista Científica

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Managing the systems which behaviour is non-deterministic is one of the most important problems in modern management theory. Today, systems with structural and behavioural complexity are prevalent in all areas of human activity, and therefore, their research is of the utmost importance. Such systems, as opposed to deterministic systems, are called non-deterministic. They are characterised by difficult predictable behaviour determined both by external random influences, and within the systems themselves. A clear example of a non-deterministic system is crowds of people, factories, and computer networks and systems. The problem of non-deterministic behaviour directly within the context of professional activities can be seen using an example of building syntactic analysers. The aim of the paper is to design a class of systems oriented towards supporting elements of a discrete event model. The target of research is to simulate discrete event models. The subject of research is a creation of a discrete event model based on the behaviour of an undetermined finite state automaton. During the preparation of the paper, there was developed and practically implemented an algorithm for the application, which materializes the principle of working with threads. The results obtained in the paper are aimed at solving the problem of parallel data processing based on the parallelism of NFA's (non-deterministic finite automaton) behaviour when reading the input string characters. As a result, this should have a positive impact on the regulation of the simulation processes of a non-deterministic system, increasing its efficiency and stability. In conclusion, the algorithm of the application work is disclosed and conclusions about the effectiveness and efficiency of its development are drawn.

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Associative Co-processor on the Basis of Programmable Logical Integrated Circuits for Special Purpose Computer Systems

Cited by 9 publications

References 8 publications

Neural Network for Handwriting Recognition

Neural Network for Handwriting Recognition

Shared Cache Based on Content Addressable Memory in a Multi-Core Architecture

Research of a multithreaded non-deterministic system model

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