Application-Specific Number Representation

Reconfigurable devices, such as Field Programmable Gate Arrays (FPGAs), enable application- specific number representations. Well-known number formats include fixed-point, floating- point, logarithmic number system (LNS), and residue number system (RNS). Such different number representations lead to different arithmetic designs and error behaviours, thus produc- ing implementations with different performance, accuracy, and cost. To investigate the design options in number representations, the first part of this thesis presents a platform that enables automated exploration of the number representation design space. The second part of the thesis shows case studies that optimise the designs for area, latency or throughput from the perspective of number representations. Automated design space exploration in the first part addresses the following two major issues: ² Automation requires arithmetic unit generation. This thesis provides optimised arithmetic library generators for logarithmic and residue arithmetic units, which support a wide range of bit widths and achieve significant improvement over previous designs. ² Generation of arithmetic units requires specifying the bit widths for each variable. This thesis describes an automatic bit-width optimisation tool called R-Tool, which combines dynamic and static analysis methods, and supports different number systems (fixed-point, floating-point, and LNS numbers). Putting it all together, the second part explores the effects of application-specific number representation on practical benchmarks, such as radiative Monte Carlo simulation, and seismic imaging computations. Experimental results show that customising the number representations brings benefits to hardware implementations: by selecting a more appropriate number format, we can reduce the area cost by up to 73.5% and improve the throughput by 14.2% to 34.1%; by performing the bit-width optimisation, we can further reduce the area cost by 9.7% to 17.3%. On the performance side, hardware implementations with customised number formats achieve 5 to potentially over 40 times speedup over software implementations

Digital signal processing application based on residue number system

Tato práce se zabývá systémem zbytkových tříd a jeho aplikacemi v digitálních obvodech. První část se zabývá VHDL návrhem různých typů sčítaček v systému zbytkových tříd a jejich porovnání se standartními sčítačkami. V druhé části je implementován obrázkový processor který pracuje v systému zbytkových tříd a jeho výkonostní analýza. V textu je popsán postup návrhu a jsou prezentovány výsledky analýz.This work deals with residue number system and its applications in digital circuits. The first part is VHDL design of different adder types in residue number system and their comparison with regular adders. The second part is VHDL implementation of image processor that computes in residue number system and its performance analysis. Presented text contains description of design procedures and presentation of analysis results.

Design and implementation of high-radix arithmetic systems based on the SDNR/RNS data representation

This project involved the design and implementation of high-radix arithmetic systems based on the hybrid SDNRIRNS data representation. Some real-time applications require a real-time arithmetic system. An SDNR/RNS arithmetic system provides parallel, real-time processing. The advantages and disadvantages of high-radix SDNR/RNS arithmetic, and the feasibility of implementing SDNR/RNS arithmetic systems in CMOS VLSI technology, were investigated in this project. A common methodological model, which included the stages of analysis, design, implementation, testing, and simulation, was followed. The combination of the SDNR and RNS transforms potential complex logic networks into simpler logic blocks. It was found that when constructing a SDNRIRNS adder, factors such as the radix, digit set, and moduli must be taken into account. There are many avenues still to explore. For example, implementing other arithmetic systems in the same CMOS VLSI technology used in this project and comparing them to equivalent SDNR/RNS systems would provide a set of benchmarks. These benchmarks would be useful in addressing issues relating to relative performance

Area-Efficient FPGA Implementation of Minimalistic Convolutional Neural Network Using Residue Number System

Convolutional Neural Networks (CNN) is the promising tool for solving task of image recognition in computer vision systems. However, the most known implementation of CNNs require a significant amount of memory for storing weights in training and work. To reduce the resource costs of CNN implementation we propose the architecture that separated on hardware and software parts for performance optimization. Also we propose to use Residue Number System (RNS) arithmetic in the hardware part which implements the convolutional layer of CNN. Software simulation using Matlab 2017b shows that CNN with a minimum number of layers can be quickly and successfully trained. Hardware simulation using FPGA Kintex7 xc7k70tfbg484-2 demonstrates that using RNS in convolutional layer of CNN allows to reduce hardware costs by 32% compared with the traditional approach based on the binary number system

Emerging Design Methodology And Its Implementation Through Rns And Qca

Digital logic technology has been changing dramatically from integrated circuits, to a Very Large Scale Integrated circuits (VLSI) and to a nanotechnology logic circuits. Research focused on increasing the speed and reducing the size of the circuit design. Residue Number System (RNS) architecture has ability to support high speed concurrent arithmetic applications. To reduce the size, Quantum-Dot Cellular Automata (QCA) has become one of the new nanotechnology research field and has received a lot of attention within the engineering community due to its small size and ultralow power. In the last decade, residue number system has received increased attention due to its ability to support high speed concurrent arithmetic applications such as Fast Fourier Transform (FFT), image processing and digital filters utilizing the efficiencies of RNS arithmetic in addition and multiplication. In spite of its effectiveness, RNS has remained more an academic challenge and has very little impact in practical applications due to the complexity involved in the conversion process, magnitude comparison, overflow detection, sign detection, parity detection, scaling and division. The advancements in very large scale integration technology and demand for parallelism computation have enabled researchers to consider RNS as an alternative approach to high speed concurrent arithmetic. Novel parallel - prefix structure binary to residue number system conversion method and RNS novel scaling method are presented in this thesis. Quantum-dot cellular automata has become one of the new nanotechnology research field and has received a lot of attention within engineering community due to its extremely small feature size and ultralow power consumption compared to COMS technology. Novel methodology for generating QCA Boolean circuits from multi-output Boolean circuits is presented. Our methodology takes as its input a Boolean circuit, generates simplified XOR-AND equivalent circuit and output an equivalent majority gate circuits. During the past decade, quantum-dot cellular automata showed the ability to implement both combinational and sequential logic devices. Unlike conventional Boolean AND-OR-NOT based circuits, the fundamental logical device in QCA Boolean networks is majority gate. With combining these QCA gates with NOT gates any combinational or sequential logical device can be constructed from QCA cells. We present an implementation of generalized pipeline cellular array using quantum-dot cellular automata cells. The proposed QCA pipeline array can perform all basic operations such as multiplication, division, squaring and square rooting. The different mode of operations are controlled by a single control line

Mathematics and Digital Signal Processing

Modern computer technology has opened up new opportunities for the development of digital signal processing methods. The applications of digital signal processing have expanded significantly and today include audio and speech processing, sonar, radar, and other sensor array processing, spectral density estimation, statistical signal processing, digital image processing, signal processing for telecommunications, control systems, biomedical engineering, and seismology, among others. This Special Issue is aimed at wide coverage of the problems of digital signal processing, from mathematical modeling to the implementation of problem-oriented systems. The basis of digital signal processing is digital filtering. Wavelet analysis implements multiscale signal processing and is used to solve applied problems of de-noising and compression. Processing of visual information, including image and video processing and pattern recognition, is actively used in robotic systems and industrial processes control today. Improving digital signal processing circuits and developing new signal processing systems can improve the technical characteristics of many digital devices. The development of new methods of artificial intelligence, including artificial neural networks and brain-computer interfaces, opens up new prospects for the creation of smart technology. This Special Issue contains the latest technological developments in mathematics and digital signal processing. The stated results are of interest to researchers in the field of applied mathematics and developers of modern digital signal processing systems

Architectures and implementations for the Polynomial Ring Engine over small residue rings

This work considers VLSI implementations for the recently introduced Polynomial Ring Engine (PRE) using small residue rings. To allow for a comprehensive approach to the implementation of the PRE mappings for DSP algorithms, this dissertation introduces novel techniques ranging from system level architectures to transistor level considerations. The Polynomial Ring Engine combines both classical residue mappings and new polynomial mappings. This dissertation develops a systematic approach for generating pipelined systolic/ semi-systolic structures for the PRE mappings. An example architecture is constructed and simulated to illustrate the properties of the new architectures. To simultaneously achieve large computational dynamic range and high throughput rate the basic building blocks of the PRE architecture use transistor size profiling. Transistor sizing software is developed for profiling the Switching Tree dynamic logic used to build the basic modulo blocks. The software handles complex nFET structures using a simple iterative algorithm. Issues such as convergence of the iterative technique and validity of the sizing formulae have been treated with an appropriate mathematical analysis. As an illustration of the use of PRE architectures for modem DSP computational problems, a Wavelet Transform for HDTV image compression is implemented. An interesting use is made of the PRE technique of using polynomial indeterminates as \u27placeholders\u27 for components of the processed data. In this case we use an indeterminate to symbolically handle the irrational number [square root of 3] of the Daubechie mother wavelet for N = 4. Finally, a multi-level fault tolerant PRE architecture is developed by combining the classical redundant residue approach and the circuit parity check approach. The proposed architecture uses syndromes to correct faulty residue channels and an embedded parity check to correct faulty computational channels. The architecture offers superior fault detection and correction with online data interruption

Algorithms and VLSI architectures for parametric additive synthesis

A parametric additive synthesis approach to sound synthesis is advantageous as it can model sounds in a large scale manner, unlike the classical sinusoidal additive based synthesis paradigms. It is known that a large body of naturally occurring sounds are resonant in character and thus fit the concept well. This thesis is concerned with the computational optimisation of a super class of form ant synthesis which extends the sinusoidal parameters with a spread parameter known as band width. Here a modified formant algorithm is introduced which can be traced back to work done at IRCAM, Paris. When impulse driven, a filter based approach to modelling a formant limits the computational work-load. It is assumed that the filter's coefficients are fixed at initialisation, thus avoiding interpolation which can cause the filter to become chaotic. A filter which is more complex than a second order section is required. Temporal resolution of an impulse generator is achieved by using a two stage polyphase decimator which drives many filterbanks. Each filterbank describes one formant and is composed of sub-elements which allow variation of the formant’s parameters. A resource manager is discussed to overcome the possibility of all sub- banks operating in unison. All filterbanks for one voice are connected in series to the impulse generator and their outputs are summed and scaled accordingly. An explorative study of number systems for DSP algorithms and their architectures is investigated. I invented a new theoretical mechanism for multi-level logic based DSP. Its aims are to reduce the number of transistors and to increase their functionality. A review of synthesis algorithms and VLSI architectures are discussed in a case study between a filter based bit-serial and a CORDIC based sinusoidal generator. They are both of similar size, but the latter is always guaranteed to be stable