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design of neuron-calculators for the normalized equivalence of two matrix arrays based on FPGA for self-learning equivalently convolutional neural networks (SLE_CNNs)

Design of neuron-calculators for the normalized equivalence ...

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Conference on Real-Time Image Processing and deep Learning

作者： Krasilenko, Vladimir G. Lazarev, Alexander A. Nikitovich, diana, V Vinnytsia Social Econ Inst 86-131 Kelestskaya Str UA-21021 Vinnytsia Ukraine Vinnytsia Natl Tech Univ Vinnytsia Ukraine

ISBN: (纸本)9781510626584

First, in the introduction, we will show the urgent need to create neuron-calculators (NCs) for the normalized equivalence of two matrix arrays for self-learning equivalently-convolutional neural networks (SLE_CNNs), video processors for parallel image processing with enhanced functionality. Consider promising areas of application of such single and multichannel neuron-calculators as high-precision, high-speed and high-performance accelerators for hardware systems and architectures for recognition, classification, image categorization, in particular for 2d-image space-invariant associative memory structures, SLE_CNNs based on the equivalence paradigm. Next, we will consider and analyze the theoretical foundations, the mathematical apparatus of matrix and continuous logic, and their basic operations, show their functional completeness, evaluate their advantages and prospects for application in the design of biologically inspired devices and systems for processing and analyzing signal arrays. We will show that some functions of continuous logic, including operations of normalized equivalence of vector and matrix signals, operation of the bounded difference of continuous logic, are the powerful basis for designing advanced accelerator calculators, micro-cells for hybrid ( mixed) analog-to-digital transformations, comparisons and calculations of characteristics. Next, we will consider in more detail the design and simulation aspects of such digital neuron-calculators for the normalized equivalence of two matrix arrays based on FPGA, including their various modifications, depending on the dimension and the number of compared arrays. We will propose our approach for calculation of the normalized equivalence functions comparing current fragments of images and filters with dimensions 3x3, 7x7, 15x15 and others. The project is executed on FPGA ALTERA MAXII. The simulation was done with Intel Quartus Prime 17 and showed that in a single chip it is possible to place four paral

关键词： self-learning convolutional neural networks optoelectronic neuron-equivalentor current mirror equivalent models continuous-logical operations 2d spatial function accelerator learning nonlinear processing

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design and simulation of array cells for image intensity transformation and coding used in mixed image processors and neural networks 12

Design and simulation of array cells for image intensity tra...

引用

12th Conference of Optics and Photonics for Information Processing

作者： Krasilenko, Vladimir G. Lazarev, Alexander A. Nikitovich, diana V. Vinnytsia Social Econ Inst 86-131 Kelestskaya Str UA-21021 Vinnytsia Ukraine Vinnytsia Natl Tech Univ Vinnytsia Ukraine

ISBN: (数字)9781510620742

ISBN: (纸本)9781510620742

The paper considers results of design and simulation of continuously logical cells (CLC) based on current mirrors (CM) with functions of preliminary analogue processing for image intensity transformation and coding for construction of mixed image processors (IP) and neural networks (NN). For such IP and NN with vector or matrix parallel inputs-outputs, it is needed active basic photosensitive cells with an extended electronic circuit, which are considered in paper. Such CLC has a number of advantages: high speed and reliability, simplicity, small power consumption, high integration level for linear and matrix structures. We show design of CLC variants for photocurrents transformation and coding and their various possible implementations and simulations. The basic element of such cells is a scheme that implements the operation of a bounded difference of continuous logic. Using a set of such circuits implemented on traditional CMOS technology, we consider generalized decomposition and other methods for designing cells for nonlinear conversion of the photocurrent intensity, which makes it easy to realize the required nonlinear conversion function. Selection of the appropriate parameters, which can be specified as constructive constants or as parameters for external control, allows changing type of synthesized functions. We also consider the applications of such parallel matrix arrays for the creation of advanced IP and NN. We show the need for various types of converting and coding the photocurrents intensity in such parallel systems and sensory devices, especially for the implementation of various types of activation functions in the hardware implementations of neural networks. Such cells consist of several dozen CMOS transistors, have low power supply voltage (1.8 divided by 3.3V), the range of an input photocurrent is 0.1o24 mu A, the transformation time is less than 1 mu s, low power consumption (microwatts). We also consider the cells for AdC after the intensity c

关键词： self-learning equivalent-convolutional neural structures equivalent models continuous-logical operations 2d spatial function neuron-equivalentor current mirror image intensity transformation nonlinear processing

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design and simulation of optoelectronic neuron-equivalentors as hardware accelerators of self-learning equivalent-convolutional neural structures (SLECNS)

Design and simulation of optoelectronic neuron-equivalentors...

引用

Conference on Neuro-Inspired Photonic Computing

作者： Krasilenko, Vladimir G. Lazarev, Alexander A. Nikitovich, diana V. Vinnytsia Social Econ Inst 86-131 Kelestskaya Str UA-21021 Vinnytsia Ukraine Vinnytsia Natl Tech Univ Vinnytsia Ukraine

ISBN: (数字)9781510619050

ISBN: (纸本)9781510619050

In the paper, we consider the urgent need to create highly efficient hardware accelerators for machine learning algorithms, including convolutional and deep neural networks (CNN and dNNS), for associative memory models, clustering, and pattern recognition. These algorithms usually include a large number of multiply-accumulate (and the like) operations. We show a brief overview of our related works the advantages of the equivalent models (EM) for describing and designing neural networks and recognizing bio-inspired systems. The capacity of NN on the basis of EM and of its modifications, including auto-and hetero-associative memories for 2d images, is in several times quantity of neurons. Such neuroparadigms are very perspective for processing, clustering. recognition, storing large size and strongly correlated and highly noised images. They are also very promising for solving the problem of creating machine uncontrolled learning. And since the basic operational functional nodes of EM are such vector-matrix or matrix-tensor procedures with continuous-logical operations as: normalized vector operations "equivalence", "nonequivalence", "auto equivalence". "auto-nonequivalence", we consider in this paper new conceptual approaches to the design of full-scale arrays of such neuron-equivalentors (NEs) with extended functionality, including different activation functions. Our approach is based on the use of analog and mixed (with special coding) methods for implementing the required operations, building NEs (with number of synapsis from 8 up to 128 and more) and their base cells, nodes based on photosensitive elements and CMOS current mirrors. We show the results of modeling the proposed new modular scalable implementations of NEs, we estimates and compare them. Simulation results show that processing time in such circuits does not exceed units of micro seconds, and for some variants 50-100 nanoseconds. Circuits are simple, have low supply voltage (1.5 - 3.3 V), low power co

关键词： self-learning equivalent-convolutional neural structures optoelectronic neuron-equivalentor current mirror equivalent models continuous-logical operations 2d spatial function accelerator teaming nonlinear processing

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