Significance of Logic Synthesis in FPGA-Based Design of Image and Signal Processing Systems

This chapter, taking FIR filters as an example, presents the discussion on efficiency of different implementation methodologies of DSP algorithms targeting modern FPGA architectures. Nowadays, programmable technology provides the possibility to implement digital systems with the use of specialized embedded DSP blocks. However, this technology gives the designer the possibility to increase efficiency of designed systems by exploitation of parallelisms of implemented algorithms. Moreover, it is possible to apply special techniques, such as distributed arithmetic (DA). Since in this approach, general-purpose multipliers are replaced by combinational LUT blocks, it is possible to construct digital filters of very high performance. Additionally, application of the functional decomposition-based method to LUT blocks optimization, and mapping has been investigated. The chapter presents results of the comparison of various design approaches in these areas

Логическая минимизация при синтезе комбинационных структур в FPGA

The paper describes the research results of application efficiency of minimization programs of functional descriptions of combinatorial logic blocks, which are included in digital devices projects that are implemented in FPGA. Programs are designed for shared and separated function minimization in a disjunctive normal form (DNF) class and minimization of multilevel representations of fully defined Boolean functions based on Shannon expansion with finding equal and inverse cofactors. The graphical form of such representations is widely known as binary decision diagrams (BDD). For technological mapping the program of "enlargement" of obtained Shannon expansion formulas was applied in a way that each of them depends on a limited number of k input variables and can be implemented on one LUT-k – a programmable unit of FPGA with k input variables. It is shown that a preliminary logic minimization, which is performed on the domestic programs, allows improving design results of foreign CAD systems such as Leonardo Spectrum (Mentor Graphics), ISE (Integrated System Environment) Design Suite and Vivado (Xilinx). The experiments were performed for FPGA families’ Virtex-II PRO, Virtex-5 and Artix-7 (Xilinx) on standard threads of industrial examples, which define both DNF systems of Boolean functions and systems represented as interconnected logical equations.Описываются результаты исследования эффективности применения программ минимизации функциональных описаний блоков комбинационной логики, входящих в проекты цифровых устройств, реализуемых в FPGA (Field-Programmable Gate Array). Программы предназначены для раздельной и совместной минимизации функций в классе ДНФ (дизъюнктивных нормальных форм) и минимизации многоуровневых представлений систем полностью определенных булевых функций на основе разложения Шеннона с нахождением как равных, так инверсных коэффициентов (кофакторов) разложения. Графические формы таких представлений широко известны в литературе как BDD (Binary Decision Diagrams). Для технологического отображения применялась программа «укрупнения» полученных формул разложения Шеннона (логических уравнений), так, чтобы  каждое из них зависело от ограниченного числа k входных переменных и могло быть реализовано на одном LUT-k ‑ программируемом элементе  FPGA, имеющем k входных переменных (LUT ‑ Look-Up Table) . Показано, что предварительная логическая минимизация , выполняемая с помощью отечественных программ, позволяет улучшать результаты проектирования в зарубежных САПР (системах автоматизированного проектирования), таких как LeonardoSpectrum (ф. Mentor Graphics)  и ISE (ф. Xilinx). Эксперименты проводились для семейств FPGA Virtex-II PRO и Virtex-5 (ф. Xilinx) на потоках стандартных промышленных примеров, задающих как системы дизъюнктивных нормальных форм булевых функций, так системы булевых функций в виде взаимосвязанных логических уравнений

Cost Effective Implementation of Fixed Point Adders for LUT based FPGAs using Technology Dependent Optimizations

Modern day field programmable gate arrays (FPGAs) have very huge and versatile logic resources resulting in the migration of their application domain from prototype designing to low and medium volume production designing. Unfortunately most of the work pertaining to FPGA implementations does not focus on the technology dependent optimizations that can implement a desired functionality with reduced cost. In this paper we consider the mapping of simple ripple carry fixed-point adders (RCA) on look-up table (LUT) based FPGAs. The objective is to transform the given RCA Boolean network into an optimized circuit netlist that can implement the desired functionality with minimum cost. We particularly focus on 6-input LUTs that are inherent in all the modern day FPGAs. Technology dependent optimizations are carried out to utilize this FPGA primitive efficiently and the result is compared against various adder designs. The implementation targets the XC5VLX30-3FF324 device from Xilinx Virtex-5 FPGA family. The cost of the circuit is expressed in terms of the resources utilized, critical path delay and the amount of on-chip power dissipated. Our implementation results show a reduction in resources usage by at least 50%; increase in speed by at least 10% and reduction in dynamic power dissipation by at least 30%. All this is achieved without any technology independent (architectural) modification

A New Approach to a FSM Implementation in Embedded Memory Blocks of FPGA Devices with Use of the Functional Decomposition

Abstrac

Advances in Functional Decomposition: Theory and Applications

Functional decomposition aims at finding efficient representations for Boolean functions. It is used in many applications, including multi-level logic synthesis, formal verification, and testing. This dissertation presents novel heuristic algorithms for functional decomposition. These algorithms take advantage of suitable representations of the Boolean functions in order to be efficient. The first two algorithms compute simple-disjoint and disjoint-support decompositions. They are based on representing the target function by a Reduced Ordered Binary Decision Diagram (BDD). Unlike other BDD-based algorithms, the presented ones can deal with larger target functions and produce more decompositions without requiring expensive manipulations of the representation, particularly BDD reordering. The third algorithm also finds disjoint-support decompositions, but it is based on a technique which integrates circuit graph analysis and BDD-based decomposition. The combination of the two approaches results in an algorithm which is more robust than a purely BDD-based one, and that improves both the quality of the results and the running time. The fourth algorithm uses circuit graph analysis to obtain non-disjoint decompositions. We show that the problem of computing non-disjoint decompositions can be reduced to the problem of computing multiple-vertex dominators. We also prove that multiple-vertex dominators can be found in polynomial time. This result is important because there is no known polynomial time algorithm for computing all non-disjoint decompositions of a Boolean function. The fifth algorithm provides an efficient means to decompose a function at the circuit graph level, by using information derived from a BDD representation. This is done without the expensive circuit re-synthesis normally associated with BDD-based decomposition approaches. Finally we present two publications that resulted from the many detours we have taken along the winding path of our research

Timing Signals and Radio Frequency Distribution Using Ethernet Networks for High Energy Physics Applications

Timing networks are used around the world in various applications from telecommunications systems to industrial processes, and from radio astronomy to high energy physics. Most timing networks are implemented using proprietary technologies at high operation and maintenance costs. This thesis presents a novel timing network capable of distributed timing with subnanosecond accuracy. The network, developed at CERN and codenamed “White- Rabbit”, uses a non-dedicated Ethernet link to distribute timing and data packets without infringing the sub-nanosecond timing accuracy required for high energy physics applications. The first part of this thesis proposes a new digital circuit capable of measuring time differences between two digital clock signals with sub-picosecond time resolution. The proposed digital circuit measures and compensates for the phase variations between the transmitted and received network clocks required to achieve the sub-nanosecond timing accuracy. Circuit design, implementation and performance verification are reported. The second part of this thesis investigates and proposes a new method to distribute radio frequency (RF) signals over Ethernet networks. The main goal of existing distributed RF schemes, such as Radio-Over-Fibre or Digitised Radio-Over-Fibre, is to increase the bandwidth capacity taking advantage of the higher performance of digital optical links. These schemes tend to employ dedicated and costly technologies, deemed unnecessary for applications with lower bandwidth requirements. This work proposes the distribution of RF signals over the “White-Rabbit” network, to convey phase and frequency information from a reference base node to a large numbers of remote nodes, thus achieving high performance and cost reduction of the timing network. Hence, this thesis reports the design and implementation of a new distributed RF system architecture; analysed and tested using a purpose-built simulation environment, with results used to optimise a new bespoke FPGA implementation. The performance is evaluated through phase-noise spectra, the Allan-Variance, and signalto- noise ratio measurements of the distributed signals

Machine learning as a service for high energy physics (MLaaS4HEP): a service for ML-based data analyses

With the CERN LHC program underway, there has been an acceleration of data growth in the High Energy Physics (HEP) field and the usage of Machine Learning (ML) in HEP will be critical during the HL-LHC program when the data that will be produced will reach the exascale. ML techniques have been successfully used in many areas of HEP nevertheless, the development of a ML project and its implementation for production use is a highly time-consuming task and requires specific skills. Complicating this scenario is the fact that HEP data is stored in ROOT data format, which is mostly unknown outside of the HEP community. The work presented in this thesis is focused on the development of a ML as a Service (MLaaS) solution for HEP, aiming to provide a cloud service that allows HEP users to run ML pipelines via HTTP calls. These pipelines are executed by using the MLaaS4HEP framework, which allows reading data, processing data, and training ML models directly using ROOT files of arbitrary size from local or distributed data sources. Such a solution provides HEP users non-expert in ML with a tool that allows them to apply ML techniques in their analyses in a streamlined manner. Over the years the MLaaS4HEP framework has been developed, validated, and tested and new features have been added. A first MLaaS solution has been developed by automatizing the deployment of a platform equipped with the MLaaS4HEP framework. Then, a service with APIs has been developed, so that a user after being authenticated and authorized can submit MLaaS4HEP workflows producing trained ML models ready for the inference phase. A working prototype of this service is currently running on a virtual machine of INFN-Cloud and is compliant to be added to the INFN Cloud portfolio of services

MIMO Systems

In recent years, it was realized that the MIMO communication systems seems to be inevitable in accelerated evolution of high data rates applications due to their potential to dramatically increase the spectral efficiency and simultaneously sending individual information to the corresponding users in wireless systems. This book, intends to provide highlights of the current research topics in the field of MIMO system, to offer a snapshot of the recent advances and major issues faced today by the researchers in the MIMO related areas. The book is written by specialists working in universities and research centers all over the world to cover the fundamental principles and main advanced topics on high data rates wireless communications systems over MIMO channels. Moreover, the book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Applied Mathematics and Computational Physics

As faster and more efficient numerical algorithms become available, the understanding of the physics and the mathematical foundation behind these new methods will play an increasingly important role. This Special Issue provides a platform for researchers from both academia and industry to present their novel computational methods that have engineering and physics applications

Cutting Edge Nanotechnology

The main purpose of this book is to describe important issues in various types of devices ranging from conventional transistors (opening chapters of the book) to molecular electronic devices whose fabrication and operation is discussed in the last few chapters of the book. As such, this book can serve as a guide for identifications of important areas of research in micro, nano and molecular electronics. We deeply acknowledge valuable contributions that each of the authors made in writing these excellent chapters