Digital optical transceiver and processing microsystem

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.

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.

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 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.

Contents-addressable memory (CAM) is a special memory that searches the input data with the entire pre-loaded database and generates corresponding address information. CAMs are advancing to be a core technology in computer networking systems. As field programmable gate array (FPGA) is recently being used for network acceleration applications, the demand to integrate CAM on FPGA is increasing. FPGA-based CAMs are divided into three categories of implementation: register-based, block RAM (BRAM)-based, and distributed RAM-based CAM. However, they come with a cost of excessive resource usage. Besides, the collision ratio is high in FPGA-based CAMs, leading to data loss and failure to produce accurate addresses. Synchronous dynamic random-access memory (SDRAM)-based CAMs, benefiting from the features of high density and low price of SDRAM, solve the limitations of FPGA’s on-chip resources. This paper proposes a data collision CAM hardware implementation using modern FPGA’s off-chip SDRAM for data storage. The hardware architecture is customized for massive lookup tables and resource-saving. Furthermore, the architecture is parameterized, which is better for integration. The synthesis results and comparisons show significant advancement compared to other FPGA-based CAM implementations by total reduction of on-chip RAM. The novel architecture shows remarkable improvement in the memory depth and width with the capacity of 128 Mbyte lookup table.

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.

Modern Field Programmable Gate Arrays (FPGAs) posses small features, and have gained popularity in mission-critical systems. However, due to small FPGA features and harsh external conditions that can be faced by a mission-critical system, an FPGA chip can suffer from faults. This in turn raises the need to test an FPGA to ensure a reliable system performance. However, a mission-critical system requires that the test process should be non-intrusive, i.e., applications & FPGA regions that are not being tested remain unaffected. Hence, an online test scheme is required that not only verifies the correctness of an FPGA, but also does not degrade the performance of other, running FPGA applications. In this paper, we propose a Hardwired Network on Chip (HWNoC) as the Test Access Mechanism (TAM). Our online test scheme uses a HWNoC, to perform real-time streaming of test data that is non-intrusive to other communication traffic. Additionally, our online test scheme exhibits approx. 18 and 29 times lower spatial and temporal overheads as compared to existing schemes, respectively.

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.

With the continuous development of The Times and technology, artificial intelligence algorithm and AI technology based on Field Programmable Gate Array (FPGA) technology have been widely used in people's life. This paper introduces the extensive application of artificial intelligence technology in production and life in recent years and its challenges. In view of the complexity of AI algorithm operation, real-time requirements and fast update and iteration characteristics, this paper proposes that FPGA chip can better solve these problems. This paper introduces the working principle of FPGA chip to realize digital processing and other hardware resources in detail, and analyzes the application advantages of FPGA in AI industry, life, and engineering. Compared with other chips, FPGA has the characteristics of low cost and flexible implementation of new algorithms, to achieve the balance between performance and cost. Finally, this paper summarizes the advantages of FPGA compared with other chips, and puts forward the possible development way and development platform in the future.

SUMMARYIn wireless communication, Viterbi decoding algorithm (VDA) is the one of most popular channel decoding algorithms, which is widely used in WLAN, WiMAX, or 3G communications. However, the throughput of Viterbi decoder is constrained by the convolutional characteristic. Recently, the three‐point VDA (TVDA) was proposed to solve this problem. In TVDA, the whole procedure can be divided into three phases, the forward, trace‐back, and decoding phases. In this paper, we analyze the parallelism of TVDA and propose parallel TVDA on the multi‐core CPU, graphics processing unit (GPU), and field programmable gate array (FPGA). We demonstrate approaches that fully exploit its performance potential on CPU, GPU, and FPGA computing platforms. For CPU platforms, we perform two optimization methods, single instruction multiple data and multithreading to gain over 145 × speedup over the naive CPU version on a quad‐core CPU platform. For GPU platforms, we propose the combination of cached memory optimization, coalesced global memory accesses, codeword packing scheme, and asynchronous data transition, achieving the throughput of 404.65 Mbps and 12 × speedup over initial GPU versions on an NVIDIA GeForce GTX580 card and 7 × speedup over Intel quad‐core CPU i5‐2300, under the same manufacturing year and both with fully optimized schemes. In addition, for FPGA platforms, we customize a radix‐4 pipelined architecture for the TVDA in a 45‐nm FPGA chip from Xilinx (XC6VLX760). Under 209.15‐MHz clock rate, it achieves a throughput of 418.30 Mbps. Finally, we also discuss the performance evaluation and efficiency comparison of different flexible architectures for real‐time Viterbi decoding in terms of the decoding throughput, power consumption, optimization schemes, programming costs, and price costs.Copyright © 2013 John Wiley & Sons, Ltd.

This paper presents realization of a Delayed Cellular Neural Network (DCNN) on field programmable gate array (FPGA). The network has two cells and a strange attractor has been found in a system described by a differential equation. In the implementation stage, Xilinx System Generator (XSG) is used and discrete time model of the network is implemented on Xilinx Spartan 3e XC3S1600e FPGA development board. The realization of DCNN on FPGA chip provides an opportunity to make the system suitable for high operating frequency applications, such as secure communication and cryptographic systems.

Free-running ADC- and FPGA-based signal processing method for brain PET using GAPD arrays

Over the years, Field Programmable Gate Arrays (FPGAs) have made a profound impact on the electronics industry with rapidly improving semiconductor-manufacturing technology ranging from sub-micron to deep sub-micron processes and equally innovative CAD tools. Though FPGA has revolutionized programmable/reconfigurable digital logic technology, one limitation of current FPGA’s is that the user is limited to strictly electronic designs. Thus, they are not suitable for applications that are not purely electronic, such as optical communications, photonic information processing systems and other multi-technology applications (ex. analog devices, MEMS devices and microwave components). Over recent years, the growing trend has been towards the incorporation of non-traditional device technologies into traditional CMOS VLSI systems. The integration of these technologies requires a new kind of FPGA that can merge conventional FPGA technology with photonic and other multi-technology devices. The proposed new class of field programmable device will extend the flexibility, rapid prototyping and reusability benefits associated with conventional electronic into photonic and multi-technology domain and give rise to the development of a wider class of programmable and embedded integrated systems. This new technology will create a tremendous opportunity for applying the conventional programmable/reconfigurable hardware concepts in other disciplines like photonic information processing. To substantiate this novel architectural concept, we have fabricated proof-of-the-concept CMOS VLSI Multi-technology FPGA (MT-FPGA) chips that include both digital field programmable logic blocks and threshold programmable photoreceivers which are suitable for sensing optical signals. Results from these chips strongly support the feasibility of this new optoelectronic device concept.