1. Virtual processor core Neuro Computer architecture Bajardi: Xidirov Diyorbek Tekshirdi: Murodullayev Baxtiyor

1. Virtual processor core 2. Neuro Computer architecture Bajardi: Yusupov Mirjalol Tekshirdi: Murodullayev Baxtiyor

A virtual core is a CPU with a separation between two areas of the processor. Virtual cores take on some of the processing of the computer without interfering with the other area. A virtual CPU (vCPU) also known as a virtual processor. It is not present on the CPU but it pretends to be present.

Virtual Core processing provides auto-detection and display of core features such as parting fractions, dip angles, lamination frequency, intensity, and more for quantitative, consistent data interpretation across wells. Cutting-edge machine learning algorithms enable automated and semi-automated methods to classify similar rocks or events on full cores. Virtual Core incorporates full core CT, photo, and processed feature detection views (including microscopic-level data) with petrophysical good log data in a comprehensive and integrated visualization and analysis environment.

Features of Virtual Core 1. Data cleansing and processing 2. Integrated viewer for well logs, images, and volumes 3. Easy navigation from hundreds of feet of core down to millimeter-resolution features 4. Machine learning with advanced detection algorithms

Let's find out how does it different from Physical core? Hyper-Threading technology creates two virtual processing cores for each physical core present in a CPU. The physical core powers the virtual cores, which then share the responsibility of task processing. Each vir,tual core is identical to the other, and though neither is as powerful as the physical core, together they far exceed the physical core power when Hyper-Threading isn't enabled. A physical core is what it is called in the name, a core that is physically on the chip. A logical core is a way those are treated that causes them to be utilized like two cores, meaning an AMD 8350 has 4 physical cores and 4 logical cores, while an i7 4760k has 4 physical cores and 0 logical cores.

The i7 quad core is 4 physical cores and 8 logical cores. A logical core is essentially how many threads it can process at the same time. A thread is a sequence of instructions. So for this i7, it can process 8 threads at the same time as ht allows 2 threads per physical core. An octa-core like the fx 8350 is 8 physical cores and 8 logical cores as it can do 8 threads. You can have a lot more threads running but the CPU will only be processing that many at a time.

Neuro Computer architecture A neurocomputer is a device for processing information based on the principles of operation of natural neural systems. [1] These principles were formalized, which allowed to speak about the theory of artificial neural networks. The problem of neurocomputers is to build real physical devices, which will allow not only to simulate artificial neural networks on a regular computer, but also to change the principles of computer operation, which makes it possible to say that they work in accordance with the theory of artificial neural networks.

Story The terms neurocybernetics, neuroinformatics, neurocomputers entered into scientific use recently - in the mid 80s of the XX century. However, the electronic and biological brains were constantly compared throughout the history of computing. The famous book of N. Wiener "Cybernetics" (1948) [2] has the subtitle "Control and communication in the animal and the machine."

Main idea – connectionism Unlike digital systems, which are combinations of processor and storage units, neuroprocessors contain memory distributed in the connections between very simple processors, which can often be described as formal neurons or blocks of similar formal neurons. Thus, the main load on the implementation of specific functions by processors rests on the system architecture, the details of which, in turn, are determined by interneuron connections. An approach based on the representation of both data memory and algorithms by a system of links (and their weights) is called connectionism.

Modern neurocomputers The long-term efforts of many research groups have resulted in a large number of different “learning rules” and architectural networks, their hardware implementations and techniques for using neural networks to solve applied problems. These intellectual inventions [10] exist as a “zoo” of neural networks. Each network of the zoo has its own architecture, the rule of learning and solves a specific set of tasks. In the last decade, serious efforts have been made to standardize the structural elements and transform this “zoo” into a “technopark” [11] : each neural network from the zoo is implemented on an ideal universal neurocomputer with a given structure.