Pipelined Two-Operand Modular Adders

Pipelined two-operand modular adder (TOMA) is one of basic components used in digital signal processing (DSP) systems that use the residue number system (RNS). Such modular adders are used in binary/residue and residue/binary converters, residue multipliers and scalers as well as within residue processing channels. The design of pipelined TOMAs is usually obtained by inserting an appriopriate number of latch layers inside a nonpipelined TOMA structure. Hence their area is also determined by the number of latches and the delay by the number of latch layers. In this paper we propose a new pipelined TOMA that is based on a new TOMA, that has the smaller area and smaller delay than other known structures. Comparisons are made using data from the very large scale of integration (VLSI) standard cell library

Residue Number System Based Building Blocks for Applications in Digital Signal Processing

Předkládaná disertační práce se zabývá návrhem základních bloků v systému zbytkových tříd pro zvýšení výkonu aplikací určených pro digitální zpracování signálů (DSP). Systém zbytkových tříd (RNS) je neváhová číselná soustava, jež umožňuje provádět paralelizovatelné, vysokorychlostní, bezpečné a proti chybám odolné aritmetické operace, které jsou zpracovávány bez přenosu mezi řády. Tyto vlastnosti jej činí značně perspektivním pro použití v DSP aplikacích náročných na výpočetní výkon a odolných proti chybám. Typický RNS systém se skládá ze tří hlavních částí: převodníku z binárního kódu do RNS, který počítá ekvivalent vstupních binárních hodnot v systému zbytkových tříd, dále jsou to paralelně řazené RNS aritmetické jednotky, které provádějí aritmetické operace s operandy již převedenými do RNS. Poslední část pak tvoří převodník z RNS do binárního kódu, který převádí výsledek zpět do výchozího binárního kódu. Hlavním cílem této disertační práce bylo navrhnout nové struktury základních bloků výše zmiňovaného systému zbytkových tříd, které mohou být využity v aplikacích DSP. Tato disertační práce předkládá zlepšení a návrhy nových struktur komponent RNS, simulaci a také ověření jejich funkčnosti prostřednictvím implementace v obvodech FPGA. Kromě návrhů nové struktury základních komponentů RNS je prezentován také podrobný výzkum různých sad modulů, který je srovnává a determinuje nejefektivnější sadu pro různé dynamické rozsahy. Dalším z klíčových přínosů disertační práce je objevení a ověření podmínky určující výběr optimální sady modulů, která umožňuje zvýšit výkonnost aplikací DSP. Dále byla navržena aplikace pro zpracování obrazu využívající RNS, která má vůči klasické binární implementanci nižší spotřebu a vyšší maximální pracovní frekvenci. V závěru práce byla vyhodnocena hlavní kritéria při rozhodování, zda je vhodnější pro danou aplikaci využít binární číselnou soustavu nebo RNS.This doctoral thesis deals with designing residue number system based building blocks to enhance the performance of digital signal processing applications. The residue number system (RNS) is a non-weighted number system that provides carry-free, parallel, high speed, secure and fault tolerant arithmetic operations. These features make it very attractive to be used in high-performance and fault tolerant digital signal processing (DSP) applications. A typical RNS system consists of three main components; the first one is the binary to residue converter that computes the RNS equivalent of the inputs represented in the binary number system. The second component in this system is parallel residue arithmetic units that perform arithmetic operations on the operands already represented in RNS. The last component is the residue to binary converter, which converts the outputs back into their binary representation. The main aim of this thesis was to propose novel structures of the basic components of this system in order to be later used as fundamental units in DSP applications. This thesis encloses improving and designing novel structures of these components, simulating and verifying their efficiency via FPGA implementation. In addition to suggesting novel structures of basic RNS components, a detailed study on different moduli sets that compares and determines the most efficient one for different dynamic range requirements is also presented. One of the main outcomes of this thesis is concluding and verifying the main condition that should be met when choosing a moduli set, in order to improve the timing performance of a DSP application. An RNS-based image processing application is also proposed. Its efficiency, in terms of timing performance and power consumption, is proved via comparing it with a binary-based one. Finally, the main considerations that should be taken into account when choosing to use the binary number system or RNS are also discussed in details.

Number Systems for Deep Neural Network Architectures: A Survey

Deep neural networks (DNNs) have become an enabling component for a myriad of artificial intelligence applications. DNNs have shown sometimes superior performance, even compared to humans, in cases such as self-driving, health applications, etc. Because of their computational complexity, deploying DNNs in resource-constrained devices still faces many challenges related to computing complexity, energy efficiency, latency, and cost. To this end, several research directions are being pursued by both academia and industry to accelerate and efficiently implement DNNs. One important direction is determining the appropriate data representation for the massive amount of data involved in DNN processing. Using conventional number systems has been found to be sub-optimal for DNNs. Alternatively, a great body of research focuses on exploring suitable number systems. This article aims to provide a comprehensive survey and discussion about alternative number systems for more efficient representations of DNN data. Various number systems (conventional/unconventional) exploited for DNNs are discussed. The impact of these number systems on the performance and hardware design of DNNs is considered. In addition, this paper highlights the challenges associated with each number system and various solutions that are proposed for addressing them. The reader will be able to understand the importance of an efficient number system for DNN, learn about the widely used number systems for DNN, understand the trade-offs between various number systems, and consider various design aspects that affect the impact of number systems on DNN performance. In addition, the recent trends and related research opportunities will be highlightedComment: 28 page

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

Design of reverse converters for the multi-moduli residue number systems with moduli of forms 2a, 2b - 1, 2c + 1

Residue number system (RNS) is a non-weighted integer number representation system that is capable of supporting parallel, carry-free and high speed arithmetic. This system is error-resilient and facilitates error detection, error correction and fault tolerance in digital systems. It finds applications in Digital Signal Processing (DSP) intensive computations like digital filtering, convolution, correlation, Discrete Fourier Transform, Fast Fourier Transform, etc. The basis for an RNS system is a moduli set consisting of relatively prime integers. Proper selection of this moduli set plays a significant role in RNS design because the speed of internal RNS arithmetic circuits as well as the speed and complexity of the residue to binary converter (R/B or Reverse Converter) have a large dependency on the form and number of the selected moduli. Moduli of forms 2a, 2b- 1, 2c + 1 (a, b and c are natural numbers) have the most use in RNS moduli sets as these moduli can be efficiently implemented using usual binary hardware that lead to simple design. Another important consideration for the reverse converter design is the selection of an appropriate conversion algorithm from Chinese Remainder Theorem (CRT), Mixed Radix Conversion (MRC) and the new Chinese Remainder Theorems (New CRT I and New CRT II). This research is focused on designing reverse converters for the multi-moduli RNS sets especially four and five moduli sets with moduli of forms 2a, 2b- 1, 2c + 1 . The residue to binary converters are designed by applying the above conversion algorithms in different possible ways and facilitating the use of modulo (2k) and modulo (2k – 1) adders that lead to simple design of adder based architectures and VLSI efficient implementations (k is a natural number). The area and delay of the proposed converters is analyzed and an efficient reverse converter is suggested from each of the various four and five moduli set converters for a given dynamic range