FPGA Theoretical Analysis and Its Advantage Comparison in Artificial Intelligence

This article is devoted to the creation of a model of observational diagnostics for patients in dentistry using artificial intelligence as an information and measurement system. Artificial intelligence technology and algorithms in the field of clinical and radiographic image analysis in general dentistry and orthopedics have become the most common systems, contributing to accurate and timely diagnosis of a particular patient. Along with clinical practice images, radiographs are key tools for graphic visualization of the patient's dentition. A model of observational diagnostics for patients using artificial intelligence technology as the latest tool in the healthcare industry can become a structured method of information standardization and measurements in general dentistry. The model of observational diagnostics can serve as a fundamental component of functional diagnostics based on artificial intelligence algorithms, since they have a special potential for increasing the reliability and consistency of diagnostic measurements and information used by a dentist during treatment planning and restoration of the dentition. The model of observational diagnostics of dental patients presented by the authors implies a significant step forward in the development of highprecision methods of functional diagnostics, monitoring and analysis of patient condition measurements. The algorithm of the observational diagnostics model was integrated into the program, the operation of which is based on artificial intelligence technology in order to increase the efficiency, consistency, optimality and accuracy of the processes carried out in clinical practice. The purpose of the study is to determine the characteristics of the model of observational diagnostics of dental patients using artificial intelligence as an information and measuring system. Main results of the study: 1) the characteristics of the practical application of artificial intelligence technology for radiographs, clinical images of patients' teeth are provided; 2) the essential aspects of the model of observational diagnostics of dental patients, as well as the features of its practical application for planning dental treatment of patients are highlighted; 3) the results of practical testing of the model of observational diagnostics of dental patients are reflected (using the example of detection and treatment of caries). Research materials: the results of academic studies on the assessment of functional diagnostics of oral cavity and dentition pathologies using artificial intelligence technology; practical testing of the model of observational diagnostics for dental patients (using the example of detection and treatment of caries in a 49yearold woman). Research methods: systematization, comparative analysis, contrastive analysis, generalization, abstractlogical method, induction, deduction, systems approach, data conceptualization, graphical and tabular visualization of data, construction of informationmeasuring systems, artificial intelligence methods, study of clinical images and radiographs of patients' dental arches. The main conclusions of the study: 1) information and measuring instruments, the operation of which is based on artificial intelligence technology, have become the basis for the rapid processing of significant amounts of information, increasing the accuracy and efficiency of clinical decisions in dentistry. These information and measuring systems allow us to determine complex patterns on radiographs and clinical images of dental arches, to increase the degree of accuracy of functional diagnostics, in particular, in the presence of subtle pathologies and disorders; 2) artificial intelligence technology cannot replace a qualified dentist, such information and measuring systems only complement the accumulated human professional experience, allowing us to jointly carry out functional diagnostics of patients with a dental profile; 3) the potential of artificial intelligence technologies to create comprehensive plans and reports for dental treatment and dental restoration forms the basis for the joint activities of the latest tools and a dentist. In the context of the study, it is noted that artificial intelligence algorithms have become a powerful informationmeasuring tool for improving the decisionmaking process in dental practice, since digital technology promptly and quickly provides feedback to the dentist about missed anomalies or suboptimal methods of dental treatment and restorative medicine. Integration of the model of observational diagnostics for dental patients will make it possible to form an algorithm for the operation of artificial intelligence that fully complies with the recommendations for functional diagnostics and dental treatment of patients, developed on the basis of factual information. The use of artificial intelligence algorithms in dental practice will increase the efficiency of the general industry standard for providing dental services to patients.

Against the background of the new era, the birth of artificial intelligence technology opened a new chapter in human development. Artificial intelligence technology has been developed in many places. The report discusses the application and innovation of artificial intelligence technology in college sports. The first thing people think of artificial intelligence is robots. In fact, it can be said that artificial intelligence can replace human labor. Artificial intelligence is a product of modern technology and cannot be understood unilaterally. For example, the various means of transportation used in people’s daily travel is an artificial intelligence that can replace people’s walking. Another example is that elevators can replace people on stairs. It is also a type of artificial intelligence, which is not an advanced technology. Product, it is also seen everywhere in people’s daily life. Artificial intelligence is a synthesis of scientific and intelligent technology created by human beings. It is a scientific technology including computer science, cognition, logic, etc., formed through mutual intersection and integration. In recent years, artificial intelligence has achieved considerable development, but there are still a series of unsolved problems, and the development of artificial intelligence is still very long. This article studies what artificial intelligence has facilitated and changed the physical education in colleges and universities, and what changes have taken place in the teaching mode are all worthy of our thinking. As a result, artificial intelligence technology is convenient for teachers and students in teaching. Let’s take a look at how to innovate.

The goal of this paper is to implement the secretion mechanism of the Thyroid Hormone (TH) based on bio-mathematical differential eqs. (DE) on an FPGA chip. Hardware Descriptive Language (HDL) is used to develop a behavioral model of the mechanism derived from the DE. The Thyroid Hormone secretion mechanism is simulated with the interaction of the related stimulating and inhibiting hormones. Synthesis of the simulation is done with the aid of CAD tools and downloaded on a Field Programmable Gate Arrays (FPGAs) Chip. The chip output shows identical behavior to that of the designed algorithm through simulation. It is concluded that the chip mimics the Thyroid Hormone secretion mechanism. The chip, operating in real-time, is computer-independent stand-alone system.

This paper discusses the development of embedded controllers on a reconfigurable multiprocessor system using field programmable gate array (FPGA) technology. The system is reconfigurable in hardware and software in the sense that certain components may be reused in different applications, hence allowing rapid development of embedded control systems. Concurrent real-time operation can be achieved by utilising hardware and software modules consisting of dedicated hardware cores and real-time operating systems. For demonstration purposes, we discuss the development of a system consisting of a network-enabled master processor that handles two slave processors each controlling a mechatronic system. The user interface is implemented using an internet browser through the master processor which allows monitoring and supervisory control of the individual systems. A multi-threaded real-time operating system runs on each of the softcore processors which allows flexibility and modularity in software design whereas pre-designed hardware modules on the FPGA chip can be utilised for building comuting hardware for control applications. Experimental results are presented for illustrating how control applications can be developed and deployed using modular components and the hardware/software environment.

This paper presents a digital implementation of 4-D autonomous hyperchaotic system on a Field Programmable Gate Array (FPGA) technology. In the design, the system parameters are selected according to the requirements for obtaining a hyperchaotic behavior. The hyperchaotic nature is theoretically verified by using bifurcation analysis and Lyapunov exponents’ calculus; and experimentally assessed by the FPGA implementation. A master-slave scheme for secure data transmission is used. Active control is applied to carry out the synchronization between the master and the slave. FPGA technology is applied in order to be implemented in digital form the 4-D continuous system using a XILINX VERTEX5 chip. The digital sequence generated from the FPGA device is observed in our experimental setup. The implementation with 32 bits fixed point is chosen; highlighting the role of synchronization mechanisms of chaotic systems in the success of these transmissions.

The type of Field Programmable Gate Arrays (FPGAs) technology and device family used in a design is a key factor for system reliability. Though antifuse-based FPGAs are widely used in aerospace because of their high reliability, current antifuse-based FPGA devices are expensive and leave no room for mistakes or changes since they are not reprogrammable. The substitute for antifuse-based FPGAs are needed in aerospace design, they should be both reprogrammable and highly reliable to Single Event Upset effects (SEUs). SRAM-based FPGAs are widely and systematically used in complex embedding digital systems both in a single chip industry and commercial applications. They are reprogrammable and high in density because of the smaller SRAM cells and logic structures. But the SRAM-based FPGAs are especially sensitive to cosmic radiation because the configuration information is stored in SRAM memory. The ideal FPGA for aerospace use should be high-density SRAM-based which is also insensitive to cosmic radiation induced SEUs. Therefore, in order to enable the use of SRAM-based FPGAs in safety critical applications, new techniques and strategies are essential to mitigate the SEU errors in such devices. In order to improve the reliability of SRAM-based FPGAs which are very sensitive to SEU errors, techniques such as reconfiguration and Triple Module Redundancy (TMR) are widely used in the aerospace electronic systems to mitigate the SEU and Single Event Functional Interrupt (SEFI) errors. Compared to reconfiguration and triplication, scrubbing and partial reconfiguration will utilize fewer or even no internal resources of FPGA. What's more, the detection and repair process can detect and correct SEU errors in configuration memories of the FPGA without affecting or interrupting the proper working of the system while reconfiguration would terminate the operation of the FPGA. This paper presents a payload system realized on Xilinx Virtex-4 FPGA which mitigates SEU effects in the internal FPGA by implementing scrubbing strategy and thus improve the reliability of the whole system.

In this paper, we introduce a new development system for creating real-time image processing hardware using custom computing machines with multiple Field Programmable Gate Array (FPGA) chips. Three distinct processes are accomplished within the development system: design entry, verification, and translation. A library of modules that implement common low-level machine vision functions is used to create complex designs based on a dataflow graph representation. The library's low-level image processing modules contain both gate-level and chip-level hardware components, of which the gate-level components are compiled into the functionality of available FPGA chips. Standard interfaces are established for input/output of the modules, allowing for the creation of sophisticated software support tools. Experimental results verify the utility of this development system for easily creating real-time machine vision hardware using multiple FPGA-based custom computing machines.

This paper presents the design and implementation of a new digital-to-time converter (DTC). The obtained resolution is 1.02 ps, and the dynamic range is about 590 ns. The experimental results indicate that the measured differential nonlinearity (DNL) and integral nonlinearity (INL) are −0.17~+0.13 LSB and −0.35~+0.62 LSB, respectively. This DTC builds coarse and fine Vernier delay lines constructed by programmable delay lines (PDLs) to ensure high performance delay. Benefited by the close-loop feedback mechanism of the PDLs’ control module, the presented DTC has excellent voltage and temperature stability. What is more, the proposed DTC can be implemented in a single field programmable gate array (FPGA) chip.

Raman spectrometer has been widely used as an identification tool for analyzing material structure and composition in many fields. However, Raman scattering echo signal is very weak, about dozens of photons at most in one laser plus signal. Therefore, it is a great challenge to design a Raman spectrum data acquisition system which could accurately receive the weak echo signal. The system designed in this paper receives optical signals with the principle of photon counter and could detect single photon. The whole system consists of a photoelectric conversion module H7421-40 and a photo counting card including a field programmable gate array (FPGA) chip and a PCI9054 chip. The module H7421-40 including a PMT, an amplifier and a discriminator has high sensitivity on wavelength from 300nm to 720nm. The Center Wavelength is 580nm which is close to the excitation wavelength (532nm), QE 40% at peak wavelength, Count Sensitivity is 7.8*10⁵(S^-1PW^-1) and Count Linearity is 1.5MHZ. In FPGA chip, the functions are divided into three parts: parameter setting module, controlling module, data collection and storage module. All the commands, parameters and data are transmitted between FPGA and computer by PCI9054 chip through the PCI interface. The result of experiment shows that the Raman spectrum data acquisition system is reasonable and efficient. There are three primary advantages of the data acquisition system: the first one is the high sensitivity with single photon detection capability; the second one is the high integrated level which means all the operation could be done by the photo counting card; and the last one is the high expansion ability because of the smart reconfigurability of FPGA chip.

The mitigation of single event upset (SEU) in SRAM based Field Programmable Gate Array (FPGA) is increasingly important as it is widely used in radiation environments such as space. As D flip-flop (DFF) and memory (including Block RAM and Configuration RAM) are the key elements in FPGAs, it is crucial to develop radiation hardening techniques for enhanced reliability of the DFF and memory. A novel hardened memory design for FPGA is proposed with multi-bit upset (MBU) protection. We further developed two prototype FPGA chips, one with and the other without SEU hardening for comparison. The FPGA chips are fabricated in a standard 0.13μm CMOS process and have a volume of 3 million equivalent logic gates. In contrast to the base FPGA, the SEU cross section of the memory in the hardened FPGA is at least three orders of magnitude lower. Also, no SEU upsets are observed in the DFF of the hardened FPGA.

The soft computing algorithms are being nowadays used for various multi input multi output complicated non linear control applications. This paper presented the development and implementation of back propagation of multilayer perceptron architecture developed in FPGA using VHDL. The usage of the FPGA (Field Programmable Gate Array) for neural network implementation provides flexibility in programmable systems. For the neural network based instrument prototype in real time application. The conventional specific VLSI neural chip design suffers the limitation in time and cost. With low precision artificial neural network design, FPGA have higher speed and smaller size for real time application than the VLSI design. The challenges are finding an architecture that minimizes the hardware cost, maximizing the performance, accuracy. The goal of this work is to realize the hardware implementation of neural network using FPGA. Digital system architecture is presented using Very High Speed Integrated Circuits Hardware Description Language (VHDL)and is implemented in FPGA chip. MATLAB ANN programming and tools are used for training the ANN. The trained weights are stored in different RAM, and is implemented in FPGA. The design was tested on a FPGA demo board. Keywords- Backpropagation, field programmable gate array (FPGA) hardware implementation, multilayer perceptron, pressure sensor, Xilinx FPGA.

In this study, an ongoing Project named FPGA Based Digital Electronics Education (FPGA_b_DEEP) is introduced and the advantages of FPGA(Field Programmable Gate Arrays) technology which brought to digital design area are dealt with by using a case study. The Project aims to develop instructive and attractive examples to teach Digital Electronics efficiently and faster. In addition to this, PBL(Project Based Learning) method and Micro Learning method are dealt with at the study separately. PBL method has been used partially at the Project and the studies on Micro Learning for association with the Project are going on recently.

The National Academies Board on Human Systems Integration (BOHSI) has organized this session exploring the state of the art and research and design frontiers relating to the application of Artificial Intelligence (AI) technologies to Healthcare. Advances in AI, machine learning, and robotics promise to revolutionize access to and quality of healthcare. At the same time lessons learned from earlier applications of AI technologies suggest a need to carefully consider the user and use context and the need for effective human-machine teaming design to avoid brittle systems that fail to be used or worse that negatively impact patient safety. The Panel brings together prominent researchers from both the Human Factors and Computer Science and AI communities to discuss promising applications, challenges and short-falls and ways forward for achieving the promise of enhanced delivery of health care through application of AI technologies.

Regular Expressions (RegExes) are widely used in various applications to identify strings of text. Their flexibility, however, increases the complexity of the detection system and often limits the detection speed as well as the total number of RegExes that can be detected using limited resources. The two classical detection methods, Deterministic Finite Automaton (DFA) and Non-Deterministic Finite Automaton (NFA), have the potential problems of prohibitively large memory requirements and a large number of concurrent operations, respectively. Although recent schemes addressing these problems to improve DFA and NFA are promising, they are inherently limited by their scalability, since they follow the state transition model in DFA and NFA, where the state transitions occur per each character of the input. We recently proposed a scalable RegEx detection system called Lookahead Finite Automata (LaFA) to solve these problems with three novel ideas: 1. Provide specialized and optimized detection modules to increase resource utilizations. 2. Systematically reordering the RegEx detection sequence to reduce number of concurrent operations. 3. Sharing states among automata for different RegExes to reduce resource requirements. In this paper, we propose an efficient hardware architecture and prototype design implementation based on LaFA. Our proof-of-concept prototype design is built on a fraction of a single commodity Field Programmable Gate Array (FPGA) chip and can accommodate up to twenty-five thousand (25k) RegExes. Using only 7% of the logic area and 25% of the memory on a Xilinx Virtex-4 FX100, the prototype design can achieve 2-Gbps (gigabits-per-second) detection throughput with only one detection engine. We estimate that 34-Gbps detection throughput can be achieved if the entire resources of a state-of-the-art FPGA chip are used to implement multiple detection engines.

The performances of System on Chip (SoC) and the Field Programmable Gate Array (FPGA) particularly, are increasing continually. Due to the growing complexity of modern embedded control systems, the need of more performance digital devices is evident. Recent FPGA technology makes it possible to include processor cores into the FPGA chip, which ensures more flexibility for digital controllers. Indeed, greater functionality of hardware and system software, Real-Time (RT) platforms and distributed subsystems are demanded. In this chapter, a design concept of FPGA based controller with Hardware/Software (Hw/Sw) codesign is proposed. It is applied for electrical machine drives. There are discussed different MultiProcessor SoC (MPSoC) architectures with Hw peripherals for the implementation on FPGA-based embedded processor cores. Hw accelerators are considered in the design to enhance the controller speed performance and reduce power consumption. Test and validation of this control system are performed on a RT motor emulator implemented on the same FPGA. Experimental results, carried on a real prototyping platform, are given in order to analyze the performance and efficiency of discussed architecture designs helping to support hard RT constraints.Request access from your librarian to read this chapter's full text.