The document outlines Reed-Solomon error correction codes. It discusses how Reed-Solomon codes encode data using a generator polynomial to produce parity check symbols. The document then describes how Reed-Solomon codes can decode errors using syndrome calculation, error location polynomials, and finding the error positions and values through algorithms like Forney's method and Chien search. Reed-Solomon codes are widely used in applications like CDs, DVDs, wireless communications and digital television for their ability to efficiently correct both random and burst errors.
This document provides information about Reed-Solomon encoding and decoding. It introduces Reed-Solomon codes, including their parameters like code length, number of information symbols, and error correcting capability. It describes the generator polynomial and how it is used to encode messages. The document also discusses the Massey FSR Synthesis Algorithm for Reed-Solomon decoding and Forney's Equation for calculating error magnitudes. An example is provided to demonstrate decoding a received codeword using the Massey algorithm.
The document describes a module called fft_16 that implements a 16 point fast Fourier transform (FFT). It takes in input signals x0 to x15 and parameters w0 to w7. It performs the FFT in 3 stages using butterfly operations defined in submodules bfly1 to bfly4. The results y0 to y15 are output based on a select signal.
DesignCon 2019 112-Gbps Electrical Interfaces: An OIF Update on CEI-112GLeah Wilkinson
DesignCon 2019
112-Gbps Electrical Interfaces: An OIF Update on CEI-112G
Brian Holden, Kandou Bus
Cathy Liu, Broadcom
Steve Sekel, Keysight
Nathan Tracy, TE Connectivity
Verilog HDL (Hardware Description Language) Training Course for self-taught instructional. User should be familiar with basic digital and logic design. Helpful to have a Verilog simulator while going through examples.
Get it right the first time lpddr4 validation and compliance testBarbara Aichinger
JEDEC LPDDR4 Compliance and Validation Testing. Learn about electrical and protocol testing and validation. DDR Memory is in almost all computing devices today.
Those slides describe digital design using Verilog HDL,
starting with Design methodologies for any digital circuit then difference between s/w (C/C++) and H/w (Verilog) and the most important constructs that let us start hardware design using Verilog HDL.
Creating Your Own PCI Express System Using FPGAs: Embedded World 2010Altera Corporation
This document discusses creating PCI Express systems using FPGA devices. It provides an overview of PCI Express, describing its key functional elements like the root complex and endpoints. It also outlines PCI Express support in Altera FPGAs, including both hard IP blocks and soft IP cores that enable PCI Express connectivity. The hard IP blocks perform the various PCI Express layers and reduce resource usage compared to soft cores.
The document outlines Reed-Solomon error correction codes. It discusses how Reed-Solomon codes encode data using a generator polynomial to produce parity check symbols. The document then describes how Reed-Solomon codes can decode errors using syndrome calculation, error location polynomials, and finding the error positions and values through algorithms like Forney's method and Chien search. Reed-Solomon codes are widely used in applications like CDs, DVDs, wireless communications and digital television for their ability to efficiently correct both random and burst errors.
This document provides information about Reed-Solomon encoding and decoding. It introduces Reed-Solomon codes, including their parameters like code length, number of information symbols, and error correcting capability. It describes the generator polynomial and how it is used to encode messages. The document also discusses the Massey FSR Synthesis Algorithm for Reed-Solomon decoding and Forney's Equation for calculating error magnitudes. An example is provided to demonstrate decoding a received codeword using the Massey algorithm.
The document describes a module called fft_16 that implements a 16 point fast Fourier transform (FFT). It takes in input signals x0 to x15 and parameters w0 to w7. It performs the FFT in 3 stages using butterfly operations defined in submodules bfly1 to bfly4. The results y0 to y15 are output based on a select signal.
DesignCon 2019 112-Gbps Electrical Interfaces: An OIF Update on CEI-112GLeah Wilkinson
DesignCon 2019
112-Gbps Electrical Interfaces: An OIF Update on CEI-112G
Brian Holden, Kandou Bus
Cathy Liu, Broadcom
Steve Sekel, Keysight
Nathan Tracy, TE Connectivity
Verilog HDL (Hardware Description Language) Training Course for self-taught instructional. User should be familiar with basic digital and logic design. Helpful to have a Verilog simulator while going through examples.
Get it right the first time lpddr4 validation and compliance testBarbara Aichinger
JEDEC LPDDR4 Compliance and Validation Testing. Learn about electrical and protocol testing and validation. DDR Memory is in almost all computing devices today.
Those slides describe digital design using Verilog HDL,
starting with Design methodologies for any digital circuit then difference between s/w (C/C++) and H/w (Verilog) and the most important constructs that let us start hardware design using Verilog HDL.
Creating Your Own PCI Express System Using FPGAs: Embedded World 2010Altera Corporation
This document discusses creating PCI Express systems using FPGA devices. It provides an overview of PCI Express, describing its key functional elements like the root complex and endpoints. It also outlines PCI Express support in Altera FPGAs, including both hard IP blocks and soft IP cores that enable PCI Express connectivity. The hard IP blocks perform the various PCI Express layers and reduce resource usage compared to soft cores.
Reed Solomon codes are a type of linear block code that can detect and correct errors in transmitted data. Reed Solomon codes operate by dividing a message into blocks, calculating redundant parity data for each block based on the message contents, and transmitting the blocks with parity. At the receiver, the redundant data enables detection and correction of errors that may have occurred during transmission. Popular examples include RS(255,223) codes that can correct up to 16 byte errors per codeword. Reed Solomon codes are widely used in storage devices and wireless communications due to their high error correction capability.
The document describes an LDPC (Low Density Parity Check) codes project done by a group of students. Key points:
- The group generated a sparse parity check matrix H for LDPC encoding that avoids cycles of length 4.
- They implemented LDPC encoding in MATLAB and Verilog, calculating parity bits from the input message bits using the formula p = (B^-1) * (Au^T).
- The Verilog implementation was tested on a Nexys-2 FPGA board, with input bits entered via switches and parity bits output to LEDs.
- The project was completed over 8 weeks. While it demonstrated LDPC encoding, the group noted the encoder has high delay and
This document provides an overview of low-density parity-check (LDPC) codes. It discusses Shannon's coding theorem and the evolution of coding technology. LDPC codes were invented by Gallager in 1963 and have simple decoding algorithms that allow them to achieve performance close to the Shannon limit. The document defines regular and irregular LDPC codes using parity check matrices and Tanner graphs. It also discusses code construction, applications of LDPC codes in wireless communications standards, and concludes that LDPC codes are becoming the mainstream in coding technology.
MIPI DevCon 2016: MIPI CSI-2 Application for Vision and Sensor Fusion SystemsMIPI Alliance
The expanding demand for imaging- and vision-based systems in mobile, IoT and automotive products is making the need for multi camera and sensor fusion systems look for novel ways to gather and process multiple data streams while still fitting into the mobile interface. The CSI-2 protocol allows camera sensor and processed image data to be combined into a single data stream using interleaving, allowing the application processor to extract the image data using the virtual channel or data type information. In this presentation, Richard Sproul of Cadence Design Systems will highlight some of the key details and requirements for a system with image processing of multi camera/sensor systems.
The document provides an overview of the responsibilities and functions of the Genie-PCIe data link layer. The data link layer is responsible for reliable transmission of transaction layer packets (TLPs) between the physical and transaction layers. It handles flow control initialization, sequencing, buffering, error detection and recovery for transmitted TLPs using ACK/NAK protocols and data link layer packets (DLLPs). The data link control state machine manages the link status and ensures proper initialization and maintenance of the link.
The document contains Verilog code for half adders and full adders. It provides two implementations for each: a half adder is implemented using either XOR and AND gates, or XOR and AND modules; a full adder is implemented using XOR, AND and OR gates arranged in a specific way to calculate the sum and carry outputs, or using XOR, AND and OR modules and a wire to decompose the calculation into steps.
The document discusses adaptive linear equalizers and turbo equalizers. It provides an overview of how adaptive linear equalizers work to compensate for inter-symbol interference caused by time-variant channels. It also describes how turbo equalizers use feedback between an equalizer and decoder to iteratively improve signal estimation. Key components of the receiver like encoders, interleavers, mappers, and the forward-backward algorithm are explained. Applications of turbo equalization in technologies like SC-FDMA, GSM, and packet data transmission are also mentioned.
This document discusses Low Density Parity Check (LDPC) codes. It describes LDPC codes as having sparse parity check matrices, which allows for large minimum distances and improved error correction performance. It explains the differences between regular and irregular LDPC codes, and discusses factors like minimum distance, cycle length, linear independence, and encoding and decoding of LDPC codes. It provides examples of parity check matrices and generator matrices. It also provides an overview of an LDPC system and the encoding process.
Performance Analysis Of Different Digital Modulation SchemeAjay Walia
This document discusses and compares different digital modulation schemes including ASK, FSK, BPSK, and QPSK. It provides details on how each scheme works including signal space representation, constellation diagrams, decision regions, and probability of error calculations. The key advantages of each scheme are highlighted, such as PSK being less susceptible to noise than ASK and QPSK providing twice the bandwidth efficiency of BPSK. References are also provided for additional information.
The document contains interview questions and answers related to CMOS design. Some key topics covered include:
1. Latch-up and how it can permanently damage a device due to excessive current flow.
2. NAND gates are preferred over NOR gates in fabrication due to higher electron mobility and lower gate leakage in NAND structures.
3. Noise margin is the minimum amount of noise that can be allowed on the input without affecting the output.
This document describes functions in VHDL. It states that a function accepts arguments and returns a result of a predetermined type. When called, actual parameters are substituted for formal parameters and the function call is replaced by the return type value. A function can define local types, constants, variables, nested functions and procedures. The keywords begin and end enclose sequential statements executed when the function is called. It then provides a simple example of an inhibit gate using a function.
This document discusses Verilog data types. There are two main groups: nets and variables. Nets like wire are used for connections and have a default value of Z. Variables like reg store values and have a default of X. Important net types are wire, tri, and trireg; wire is used for single-driver nets while tri is for multiple drivers. Variables include reg for storage in procedural blocks, integer for integers, and real for real numbers.
This document discusses Verilog HDL as a solution for designing digital circuits. It provides an overview of traditional design approaches like gate-level and schematic design and their limitations for large, complex designs. Verilog HDL was developed in the 1980s to provide a simple, intuitive way to describe digital circuits for modeling, simulation, and analysis. It allows a top-down design approach with modules that have well-defined interfaces and behaviors. The document covers various coding styles in Verilog like structural, dataflow, and behavioral, as well as concepts like ports, parameters, nets, registers, delays, and test benches. It provides examples of memory operations and emphasizes thinking concurrently when writing Verilog code.
A typical design flow follows the below structure and can be broken down into multiple steps. Some of these phases happen in parallel and some in sequentially.
Requirements
A customer of a semiconductor firm is typically some other company who plans to use the chip in its systems or end products. So, the customer's requirements also play an important role in deciding how the chip should be designed.
The first step is to collect the requirements, estimate the end product's market value, and evaluate the number of resources required to do the project.
Specifications
The next step is to collect specifications that describe the functionality, interface abstractly, and over all architecture of the chip to be designed. This can be something along the lines such as:
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Requires computational power to run imaging algorithms to support virtual reality.
Requires two ARM A53 processors with coherent interconnect and should run at 600 MHz.
Requires USB 3.0, Bluetooth, and PCIe 2nd gen interfaces.
It should support 1920x1080 pixel displays with an appropriate controller.
Digital Design
Because of the complex nature of modern chips, it's impossible to build something from scratch, and in many cases, many components will be reused.
For example, company A requires a FlexCAN module to interact with other modules in an automobile. They can either buy the FlexCAN design from another company to save time and effort or spend resources to build one.
It's not practical to design such a system from basic building blocks such as flip-flops and CMOS transistors.
Instead, a behavioral description is developed to analyze the design in terms of functionality, performance, and other high-level issues using a Hardware Description Language such as Verilog or VHDL.
This is usually done by a digital designer and is similar to a high-level computer programmer equipped with digital electronics skills.
Verification
Once the RTL design is ready, it needs to be verified for functional correctness.
For example, a DSP processor is expected to issue bus transactions with fetching instructions from memory and know that this will happen as expected.
The functional verification is required at this point, which is done with EDA simulators' help that can model the design and apply a different stimulus to it. This is the job of a pre-silicon verification engineer.
Logic Synthesis
Now we will convert this design into hardware schematic with real elements such as combinational gates and flip-flops. This step is called synthesis.
Logic synthesis tools enable the conversion of RTL description in HDL to a gate-level netlist. This netlist is a description of the circuit in terms of gates and connections between them.
Logic synthesis tools ensure that the netlist meets timing, area, and power specifications. Typically, they have access to different technology node
Convolution codes and turbo codes are error-correcting codes used to reliably transmit digital data over noisy communication channels. Convolution codes work by convolving the sequence of information bits according to some rule, spreading the bits along the coded sequence. Turbo codes achieve better error correction than convolution codes through the parallel concatenation of convolutional encoders separated by an interleaver, along with iterative decoding. The Viterbi algorithm is typically used to decode convolution codes by finding the most probable state sequence, while turbo codes use two decoders and feedback to iteratively improve the decoding of each encoded sequence.
This document presents a unified approach to analyzing prescalers and dual modulus prescalers for low-power systems. It introduces various flip-flop circuit techniques like true single phase clock, extended true single phase clock, and hybrid master slave flip-flop that can be used to build prescalers. Prescalers are frequency dividers that can divide an input frequency by an integer value. Dual modulus prescalers can divide by two integers like N and N+1. The document discusses the construction of divide-by-N prescalers and divide-by-N/N+1 dual modulus prescalers. It then shows simulation results of various prescalers and compares the performance of the proposed divide
Memory ECC - The Comprehensive of SEC-DED. Sk Cheah
The document discusses error correction codes (ECC) used for dynamic random access memory (DRAM). It introduces how ECC is implemented using a memory controller on the processor. It describes the academic background of Hamming codes, including the commonly used (72,64) single error correction, double error detection (SECDED) code. It also discusses optimizations of the (72,64) SECDED code implemented in industrial systems to simplify the logic and reduce gate counts.
This document provides a summary of key concepts from a college algebra textbook, including:
Rational expressions involve fractions of polynomials. The domain of an algebraic expression is the set of real numbers for which the expression is defined. Compound fractions contain fractions in the numerator, denominator, or both. Rational expressions can be simplified by factoring and canceling common factors. Adding and subtracting rational expressions requires finding a common denominator. The denominator of a fraction can be rationalized by multiplying the numerator and denominator by the conjugate radical. Common errors involve applying properties of multiplication to addition incorrectly.
This document discusses the fixed point iteration method for solving nonlinear equations numerically. It begins with an overview of the method, explaining that it involves rewriting equations in the form x=g(x) and then iteratively calculating xn+1=g(xn) until convergence. The document then provides an example of using the method to solve the equation x3+x2-1=0. It shows rewriting the equation, choosing an initial guess, iteratively calculating the next value of x, and checking for convergence. The document concludes by explaining how to implement the fixed point iteration method numerically using loops in code.
Reed Solomon codes are a type of linear block code that can detect and correct errors in transmitted data. Reed Solomon codes operate by dividing a message into blocks, calculating redundant parity data for each block based on the message contents, and transmitting the blocks with parity. At the receiver, the redundant data enables detection and correction of errors that may have occurred during transmission. Popular examples include RS(255,223) codes that can correct up to 16 byte errors per codeword. Reed Solomon codes are widely used in storage devices and wireless communications due to their high error correction capability.
The document describes an LDPC (Low Density Parity Check) codes project done by a group of students. Key points:
- The group generated a sparse parity check matrix H for LDPC encoding that avoids cycles of length 4.
- They implemented LDPC encoding in MATLAB and Verilog, calculating parity bits from the input message bits using the formula p = (B^-1) * (Au^T).
- The Verilog implementation was tested on a Nexys-2 FPGA board, with input bits entered via switches and parity bits output to LEDs.
- The project was completed over 8 weeks. While it demonstrated LDPC encoding, the group noted the encoder has high delay and
This document provides an overview of low-density parity-check (LDPC) codes. It discusses Shannon's coding theorem and the evolution of coding technology. LDPC codes were invented by Gallager in 1963 and have simple decoding algorithms that allow them to achieve performance close to the Shannon limit. The document defines regular and irregular LDPC codes using parity check matrices and Tanner graphs. It also discusses code construction, applications of LDPC codes in wireless communications standards, and concludes that LDPC codes are becoming the mainstream in coding technology.
MIPI DevCon 2016: MIPI CSI-2 Application for Vision and Sensor Fusion SystemsMIPI Alliance
The expanding demand for imaging- and vision-based systems in mobile, IoT and automotive products is making the need for multi camera and sensor fusion systems look for novel ways to gather and process multiple data streams while still fitting into the mobile interface. The CSI-2 protocol allows camera sensor and processed image data to be combined into a single data stream using interleaving, allowing the application processor to extract the image data using the virtual channel or data type information. In this presentation, Richard Sproul of Cadence Design Systems will highlight some of the key details and requirements for a system with image processing of multi camera/sensor systems.
The document provides an overview of the responsibilities and functions of the Genie-PCIe data link layer. The data link layer is responsible for reliable transmission of transaction layer packets (TLPs) between the physical and transaction layers. It handles flow control initialization, sequencing, buffering, error detection and recovery for transmitted TLPs using ACK/NAK protocols and data link layer packets (DLLPs). The data link control state machine manages the link status and ensures proper initialization and maintenance of the link.
The document contains Verilog code for half adders and full adders. It provides two implementations for each: a half adder is implemented using either XOR and AND gates, or XOR and AND modules; a full adder is implemented using XOR, AND and OR gates arranged in a specific way to calculate the sum and carry outputs, or using XOR, AND and OR modules and a wire to decompose the calculation into steps.
The document discusses adaptive linear equalizers and turbo equalizers. It provides an overview of how adaptive linear equalizers work to compensate for inter-symbol interference caused by time-variant channels. It also describes how turbo equalizers use feedback between an equalizer and decoder to iteratively improve signal estimation. Key components of the receiver like encoders, interleavers, mappers, and the forward-backward algorithm are explained. Applications of turbo equalization in technologies like SC-FDMA, GSM, and packet data transmission are also mentioned.
This document discusses Low Density Parity Check (LDPC) codes. It describes LDPC codes as having sparse parity check matrices, which allows for large minimum distances and improved error correction performance. It explains the differences between regular and irregular LDPC codes, and discusses factors like minimum distance, cycle length, linear independence, and encoding and decoding of LDPC codes. It provides examples of parity check matrices and generator matrices. It also provides an overview of an LDPC system and the encoding process.
Performance Analysis Of Different Digital Modulation SchemeAjay Walia
This document discusses and compares different digital modulation schemes including ASK, FSK, BPSK, and QPSK. It provides details on how each scheme works including signal space representation, constellation diagrams, decision regions, and probability of error calculations. The key advantages of each scheme are highlighted, such as PSK being less susceptible to noise than ASK and QPSK providing twice the bandwidth efficiency of BPSK. References are also provided for additional information.
The document contains interview questions and answers related to CMOS design. Some key topics covered include:
1. Latch-up and how it can permanently damage a device due to excessive current flow.
2. NAND gates are preferred over NOR gates in fabrication due to higher electron mobility and lower gate leakage in NAND structures.
3. Noise margin is the minimum amount of noise that can be allowed on the input without affecting the output.
This document describes functions in VHDL. It states that a function accepts arguments and returns a result of a predetermined type. When called, actual parameters are substituted for formal parameters and the function call is replaced by the return type value. A function can define local types, constants, variables, nested functions and procedures. The keywords begin and end enclose sequential statements executed when the function is called. It then provides a simple example of an inhibit gate using a function.
This document discusses Verilog data types. There are two main groups: nets and variables. Nets like wire are used for connections and have a default value of Z. Variables like reg store values and have a default of X. Important net types are wire, tri, and trireg; wire is used for single-driver nets while tri is for multiple drivers. Variables include reg for storage in procedural blocks, integer for integers, and real for real numbers.
This document discusses Verilog HDL as a solution for designing digital circuits. It provides an overview of traditional design approaches like gate-level and schematic design and their limitations for large, complex designs. Verilog HDL was developed in the 1980s to provide a simple, intuitive way to describe digital circuits for modeling, simulation, and analysis. It allows a top-down design approach with modules that have well-defined interfaces and behaviors. The document covers various coding styles in Verilog like structural, dataflow, and behavioral, as well as concepts like ports, parameters, nets, registers, delays, and test benches. It provides examples of memory operations and emphasizes thinking concurrently when writing Verilog code.
A typical design flow follows the below structure and can be broken down into multiple steps. Some of these phases happen in parallel and some in sequentially.
Requirements
A customer of a semiconductor firm is typically some other company who plans to use the chip in its systems or end products. So, the customer's requirements also play an important role in deciding how the chip should be designed.
The first step is to collect the requirements, estimate the end product's market value, and evaluate the number of resources required to do the project.
Specifications
The next step is to collect specifications that describe the functionality, interface abstractly, and over all architecture of the chip to be designed. This can be something along the lines such as:
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Current TimeÂ
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DurationÂ
18:10
Â
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Requires computational power to run imaging algorithms to support virtual reality.
Requires two ARM A53 processors with coherent interconnect and should run at 600 MHz.
Requires USB 3.0, Bluetooth, and PCIe 2nd gen interfaces.
It should support 1920x1080 pixel displays with an appropriate controller.
Digital Design
Because of the complex nature of modern chips, it's impossible to build something from scratch, and in many cases, many components will be reused.
For example, company A requires a FlexCAN module to interact with other modules in an automobile. They can either buy the FlexCAN design from another company to save time and effort or spend resources to build one.
It's not practical to design such a system from basic building blocks such as flip-flops and CMOS transistors.
Instead, a behavioral description is developed to analyze the design in terms of functionality, performance, and other high-level issues using a Hardware Description Language such as Verilog or VHDL.
This is usually done by a digital designer and is similar to a high-level computer programmer equipped with digital electronics skills.
Verification
Once the RTL design is ready, it needs to be verified for functional correctness.
For example, a DSP processor is expected to issue bus transactions with fetching instructions from memory and know that this will happen as expected.
The functional verification is required at this point, which is done with EDA simulators' help that can model the design and apply a different stimulus to it. This is the job of a pre-silicon verification engineer.
Logic Synthesis
Now we will convert this design into hardware schematic with real elements such as combinational gates and flip-flops. This step is called synthesis.
Logic synthesis tools enable the conversion of RTL description in HDL to a gate-level netlist. This netlist is a description of the circuit in terms of gates and connections between them.
Logic synthesis tools ensure that the netlist meets timing, area, and power specifications. Typically, they have access to different technology node
Convolution codes and turbo codes are error-correcting codes used to reliably transmit digital data over noisy communication channels. Convolution codes work by convolving the sequence of information bits according to some rule, spreading the bits along the coded sequence. Turbo codes achieve better error correction than convolution codes through the parallel concatenation of convolutional encoders separated by an interleaver, along with iterative decoding. The Viterbi algorithm is typically used to decode convolution codes by finding the most probable state sequence, while turbo codes use two decoders and feedback to iteratively improve the decoding of each encoded sequence.
This document presents a unified approach to analyzing prescalers and dual modulus prescalers for low-power systems. It introduces various flip-flop circuit techniques like true single phase clock, extended true single phase clock, and hybrid master slave flip-flop that can be used to build prescalers. Prescalers are frequency dividers that can divide an input frequency by an integer value. Dual modulus prescalers can divide by two integers like N and N+1. The document discusses the construction of divide-by-N prescalers and divide-by-N/N+1 dual modulus prescalers. It then shows simulation results of various prescalers and compares the performance of the proposed divide
Memory ECC - The Comprehensive of SEC-DED. Sk Cheah
The document discusses error correction codes (ECC) used for dynamic random access memory (DRAM). It introduces how ECC is implemented using a memory controller on the processor. It describes the academic background of Hamming codes, including the commonly used (72,64) single error correction, double error detection (SECDED) code. It also discusses optimizations of the (72,64) SECDED code implemented in industrial systems to simplify the logic and reduce gate counts.
This document provides a summary of key concepts from a college algebra textbook, including:
Rational expressions involve fractions of polynomials. The domain of an algebraic expression is the set of real numbers for which the expression is defined. Compound fractions contain fractions in the numerator, denominator, or both. Rational expressions can be simplified by factoring and canceling common factors. Adding and subtracting rational expressions requires finding a common denominator. The denominator of a fraction can be rationalized by multiplying the numerator and denominator by the conjugate radical. Common errors involve applying properties of multiplication to addition incorrectly.
This document discusses the fixed point iteration method for solving nonlinear equations numerically. It begins with an overview of the method, explaining that it involves rewriting equations in the form x=g(x) and then iteratively calculating xn+1=g(xn) until convergence. The document then provides an example of using the method to solve the equation x3+x2-1=0. It shows rewriting the equation, choosing an initial guess, iteratively calculating the next value of x, and checking for convergence. The document concludes by explaining how to implement the fixed point iteration method numerically using loops in code.
The document discusses digital systems and binary numbers. It defines digital systems as systems that manipulate discrete elements of information, such as binary digits represented by the values 0 and 1. It explains how binary numbers are represented and arithmetic operations like addition, subtraction, multiplication and division are performed on binary numbers. It also discusses number base conversions between decimal, binary, octal and hexadecimal numbering systems. Finally, it covers binary complements including 1's complement, 2's complement and subtraction using complements.
The document discusses digital and analog systems. It explains that digital systems represent information as discrete values using bits, whereas analog systems represent information as continuous values. It provides examples of digital and analog signals and discusses how a continuous analog signal can be converted to a discrete digital signal through sampling and quantization. It also covers binary, octal, and hexadecimal number systems and how to convert between them. Finally, it discusses binary addition and subtraction using complement representations.
Digital systems represent information using discrete binary values of 0 and 1 rather than continuous analog values. Binary numbers use a base-2 numbering system with place values that are powers of 2. There are various number systems like decimal, binary, octal and hexadecimal that use different number bases and represent the same number in different ways. Complements are used in binary arithmetic to perform subtraction by adding the 1's or 2's complement of a number. The 1's complement is obtained by inverting all bits, while the 2's complement is obtained by inverting all bits and adding 1.
Universal Coding of the Reals: Alternatives to IEEE Floating Pointinside-BigData.com
In this video from the CoNGA conference in Singapore, Peter Lindstrom from Lawrence Livermore National Laboratory presents: Universal Coding of the Reals: Alternatives to IEEE Floating Point.
"We propose a modular framework for representing the real numbers that generalizes IEEE, POSITS, and related floating-point number systems, and which has its roots in universal codes for the positive integers such as the Elias codes. This framework unifies several known but seemingly unrelated representations within a single schema while also introducing new representations. We particularly focus on variable-length encoding of the binary exponent and on the manner in which fraction bits are mapped to values. Our framework builds upon and shares many of the attractive properties of POSITS but allows for independent experimentation with exponent codes, fraction mappings, reciprocal closure, rounding modes, handling of under- and overflow, and underlying precision."
Watch the video: https://wp.me/p3RLHQ-iuk
Learn more: https://posithub.org/conga/2018/techprogramme
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
1) Testing digital circuits involves detecting faults that could lead to failures or errors. Common faults include stuck-at faults where a line is stuck at logic 0 or 1.
2) Test patterns are generated to detect faults by sensitizing a path from the fault to an output and justifying input values. Boolean difference and D-algorithms are used.
3) Faults in PLA circuits include growth, disappearance, shrinkage and appearance faults affecting the AND and OR planes. Their effect is modeled and tests generated.
4) Fault tolerance techniques include avoidance, detection using redundancy, masking using voting, and dynamic reconfiguration to replace faulty components. Error detecting and correcting codes are also used.
This document provides an introduction to Reed-Solomon codes, which are word-oriented, non-binary BCH codes that are simple, robust, and perform well for burst errors. Reed-Solomon codes use Galois field theory to encode data into blocks of length 2^m - 1 by adding 2t parity check words, allowing the correction of t errors. Encoding involves dividing the data by a generator polynomial to calculate parity bits, while decoding uses syndromes to find error locations and magnitudes to recover the original data. Key algorithms for decoding include Berlekamp-Massey for finding error locations, Chien search to factor roots, and Forney's algorithm to find error values.
This document provides an introduction to Reed-Solomon codes, which are word-oriented, non-binary BCH codes that are simple, robust, and perform well for burst errors. Reed-Solomon codes use Galois field techniques to encode data into blocks of length 2^m - 1 by adding 2t parity check words, allowing the correction of t errors. The encoding and decoding procedures make use of a generator polynomial, Berlekamp-Massey algorithm, Chien search, and Forney algorithm. Future work may include more flexible generator polynomials or converting C54x codes to C55x codes.
This document introduces Reed-Solomon codes. It describes Reed-Solomon codes as word-oriented, non-binary BCH codes that are simple, robust, and perform well for burst errors. It explains that a Reed-Solomon code block length is N=2m-1, with a capacity to correct t errors using 2t parity check words. The document outlines the generator polynomial, encoding, decoding procedures including syndrome calculation, Berlekamp-Massey algorithm, Chien search, and Forney algorithm. It concludes with potential future tasks related to Reed-Solomon codes.
This document discusses randomized algorithms. It begins by listing different categories of algorithms, including randomized algorithms. Randomized algorithms introduce randomness into the algorithm to avoid worst-case behavior and find efficient approximate solutions. Quicksort is presented as an example randomized algorithm, where randomness improves its average runtime from quadratic to linear. The document also discusses the randomized closest pair algorithm and a randomized algorithm for primality testing. Both introduce randomness to improve efficiency compared to deterministic algorithms for the same problems.
Defense Senior College on Error Coding presentation 4/22/2010Felicia Fort, MBA
This document provides an overview of error detecting and error correcting codes. It defines key terms like check digits, linear codes, and encoding and decoding. Check digits are added to identifiers like credit card numbers and zip codes to detect errors. Linear codes are a type of error correcting code that represent messages as vectors. Encoding a linear code involves a generator matrix, while decoding uses a parity check matrix. The document gives examples of encoding and decoding a sample linear code and discusses the advantages and disadvantages of linear codes.
The document summarizes a presentation titled "Yoyak" given by Heejong Lee at ScalaDays 2015. The presentation introduces Yoyak, a static analysis framework developed by the speaker. It covers the following topics:
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2. Abstraction
• Reed-Solomon code RS(544,514) is applied in 200/400GBASE-R
FEC(Forward Error Correction).
• The big picture of the coding math is like the following:
514-symbol
block
bits stream
514-symbol
message block
30-symbol
parity block
divide by and
keep the remainder
g(x)
544-symbol codeword block 544-symbol received block
Errors
Syndromes
Calculate the syndromes with g(x)
Error location
Error locator polynomial
544-symbol Corrected codeword
Error value
514-symbol message
drop parity block
Encoder Decoder
3. Finite Field
• Reed-Solomon algorithm is defined on finite field. So what is that?
• First of all, the ‘field’ is defined as a set with the following character:
• two operations ‘Addition’ and ‘Multiplication’ defined on it.
• closed on these operations.
• commutative, associative and distributive laws hold.
• additive and multiplicative identity elements.
• additive inverse for every element.
• multiplicative inverse for every non-zero element.
• ‘Real number’ set is a field.
• ‘Integer’ set is not a field. (e.g. Number 2 doesn’t have a
multiplicative inverse which is also an integer)
4. Finite Field
• The ‘Finite field’ is a field with finite elements, as it says in its
name. (Finite field is also called ‘Galois field’ named after
Evariste Galois (1811–1832))
• How can a field be ‘finite’? The key is ‘modulo’. Operations
become ‘addition with modulo ’ and multiplication with
modulo ’. ( is short for prime)
• An example, a finite field has 3 elements: 0, 1, 2.
Operations with modulo 3. The operations meet all the items in
field definition.
• or is a finite field if is a prime number.
p
p p
GF(3)
GF(p) GF(pm
) p
1 + 2 = 3 (mod 3) = 0
2 × 2 = 4 (mod 3) = 1
5. Finite Field
• About the prime number .
• integer set {0, 1, 2} is a finite field,
called . (3 is a prime number)
• integer set {0, 1, 2, 3} is not a finite
field, (4 is not a prime number),
because element 2 doesn’t have a
multiplicative inverse. So there’s
no .
• But we can make the field with 4
elements a finite field if we change the
rules a little
• a set with four elements {00, 01, 10,
11} is a finite field, called
p
GF(3)
GF(4)
GF(22
)
+ 0 1 2
0 0 1 2
1 1 2 0
2 2 0 1
x 0 1 2
0 0 0 0
1 0 1 2
2 0 2 1
+ 0 1 2 3
0 0 1 2 3
1 1 2 3 0
2 2 3 0 1
3 3 0 1 2
x 0 1 2 3
0 0 0 0 0
1 0 1 2 3
2 0 2 0 2
3 0 3 2 1
+ 00 01 10 11
00 00
01 10 11
01 01 00 11 10
10 10 11 00 01
11 11 10 01 00
x 00 01 10 11
00 00 00 00 00
01 00 01 10 11
10 00 10 11 01
11 00 11 01 10
6. Finite Field
• Here we introduce the ‘Polynomial’. We can use polynomials
to present the elements in with coefficients from
.
• Operations will be like the following: (Note that the
coefficients are from )
GF (2m
)
GF (2)
GF (2)
11000001 : x7
+ x6
+ 1 = 1x7
+ 1x6
+ 0x5
+ 0x4
+ 0x3
+ 0x2
+ 0x1
+ 1x0
Addition Multiplication
7. Finite Field
• Then we get back to the add and multiply operation matrix
in .
• Represent the elements with polynomials
• We should use modulo to keep the elements ‘finite’. And the
polynomial is used, which also called the
primitive polynomial.
• Take the red cell for example:
GF(22
)
x2
+ x + 1
+ 00 01 10 11
00 00
01 10 11
01 01 00 11 10
10 10 11 00 01
11 11 10 01 00
x 00 01 10 11
00 00 00 00 00
01 00 01 10 11
10 00 10 11 01
11 00 11 01 10 00 ⇒ 0x + 0
01 ⇒ 0x + 1
10 ⇒ 1x + 0
11 ⇒ 1x + 1
x + 1
× x
= x2
+ x (mod x2
+ x + 1)
= 1
8. Finite Field
• Another character is, let be the primitive element, then we can use power of this
element to generate all the non-zero elements in the finite field . See the following
example in :
α = x
GF (2m
)
GF (24
)
Similar polynomial also used for PRBS
Prime polynomial
9. Encoder
• 400GBASE-R module FEC encoder defined in IEEE 802.3 119.2.4.6.
• RS(544,514). Reed-Solomon code
• k=514 message symbols, code word length 544 symbols
• m=10 means each symbol has 10 bits.
• 2t=30 symbols, can correct t=15 symbols.
10. Encoder
• The big picture of the encoder is divide the message polynomial
(multiply ) by the generating polynomial , then use the remainder
as parity to combine with message to get the final code word.
x2t
g(x)
p(x)
mk−1xn−1
+ ⋯ + m1x2t+1
+ m0x2t
+ 0x2t−1
+ ⋯ + 0x + 0
% g2tx2t
+ ⋯ + g1x + g0
p2t−1x2t−1
+ ⋯ + p1x + p0
(mk−1xk−1
+ ⋯ + m1x + m0) × x2t
=
g(x) =
2t−1
∏
j=0
(x − αj
)
mk−1xn−1
+ ⋯ + m1x2t+1
+ m0x2t
+ p2t−1x2t−1
+ ⋯ + p1x + p0
Message symbols Parity symbols
Code word symbols
11. Encoder - Example
• Let’s take a simple example to look
into the division operation.
• Message is {1,2,3,4,5,6,7,8,9,10,11}
• Parity is {3,3,12,12}
• The final code word is
{1,2,3,4,5,6,7,8,9,10,11,3,3,12,12}
• Generating polynomial
• Prime polynomial is
• RS(15,11) on
g(x) = x4
+ 15x3
+ 3x2
+ x + 12
x4
+ x + 1
GF(24
)
12. Encoder
• IEEE gives the definition of generating polynomial .
• Use the simple one in last page to see how the calculation done.
g(x)
g(x) =
2t−1
∏
j=0
(x − αj
) = g2tx2t
+ ⋯ + g1x + g0
g(x) =
3
∏
j=0
(x − 2j
) = (x − 1) (x − 2) (x − 4) (x − 8)
= (x2
− 3x + 2) (x − 4) (x − 8)
= (x3
− 7x2
+ 14x − 8) (x − 8)
= (x4
− 7x3
+ 14x2
− 8x − 8x3
+ 13x2
− 9x + 12)
= x4
+ 15x3
+ 3x2
+ x + 12
7 × 8 = (x2
+ x + 1)x3
= x5 + x4 + x3
= x(x + 1) + (x + 1) + x3
= x2
+ x + x + 1 + x3
= x3
+ x2
+ 1
= 13
−7 − 8 = 7 + 8
= (x2
+ x + 1) + x3
= x3
+ x2
+ x + 1
= 15
• Keep in mind that all the addition and multiplication are defined in GF(24
)
13. Encoder
• We can calculate the using the same method with the following
parameters:
• Coefficients calculated with python(use modified pyfinite lib).
• Coefficients in IEEE.
gi
α = 2 x10
+ x3
+ 1
Exactly the same
(Primitive polynomial)
14. Encoder
• After we figure out all the detail of the calculation, let’s hold on a moment
to see what’s the mathematical idea behind the calculation.
• The final codeword is combined with message and parity symbols. And
the parity comes from the remainder. So the codeword can be divided by
. That means are roots of the codeword polynomial.g(x) αj
% = 0
2t−1
∏
j=0
(x − αj
)mk−1xn−1
+ ⋯ + m1x2t+1
+ m0x2t
+ p2t−1x2t−1
+ ⋯ + p1x + p0
Message symbols Parity symbols
C(x) = cn−1xn−1
+ ⋯ + c1x + c0
C(αj
) = CHT
= [c0 c1 ⋯ cn−1]
1 1 1 ⋯ 1
1 α1
α2
⋯ α2t−1
1 α2
α4
⋯ α(2t−1)2
⋯ ⋯ ⋯ ⋯ ⋯
1 αn−1
α2(n−1)
⋯ α(2t−1)(n−1)
= O1×2t
• Rewrite the equation in matrix
15. Encoder
• This equation means the codeword subspace is perpendicular
to the ’s row space.HT
[c0 c1 ⋯ cn−1]
1 1 1 ⋯ 1
1 α1
α2
⋯ α2t−1
1 α2
α4
⋯ α(2t−1)2
⋯ ⋯ ⋯ ⋯ ⋯
1 αn−1
α2(n−1)
⋯ α(2t−1)(n−1)
= O1×2t
• The encoder mapping the -dimensional original message into
-dimensional space. The -dimensional codeword subspace is
perpendicular to -dimensional subspace.
• If the transmission introduce any error make to , then
they’re not perpendicular any more. Then we know there are
errors, and maybe we can correct the errors.
k mk
n k
2t (2t = n − k)
C(x) R(x)
16. Decoder
• The result of is no longer Zero-matrix if there are errors.
• The result will be S. (short for Syndromes)
RHT
[r0 r1 ⋯ rn−1]
1 1 1 ⋯ 1
1 α1
α2
⋯ α2t−1
1 α2
α4
⋯ α(2t−1)2
⋯ ⋯ ⋯ ⋯ ⋯
1 αn−1
α2(n−1)
⋯ α(2t−1)(n−1)
= S
• We can correct the message if we know the error locations and
error values.
17. Decoder
• If there are errors.
• We can see that the ‘Syndromes’ are only affected by errors, no
matter what the codeword is.
• If there are errors and , then we can find the errors and
correct them.
• What we are going to do is to solve equations to get errors location
and errors value , with the known ‘Syndromes’
v v ≤ t
Xj Yj Si
R(x) = C(x) + E(x)
R(αi
) = C(αi
) + E(αi
) = 0 + E(αi
) = Si
Si = E(αi
)
= Y1αie1 + Y2αie2 + ⋯ + Yvαiev
= Y1Xi
1 + Y2Xi
2 + ⋯ + YvXi
v =
v
∑
j=1
YjXi
j
18. Decoder - Error locator
• Errors locator polynomial
• Since are roots of this equation
• With , we got equation group with equations.
And solve the equation group to get the coefficients
X−1
j
i = (0,1,⋯, v − 1) v
(Λ1, Λ2, ⋯, Λv)
Λ(x) = (1 + X1x)(1 + X2x)⋯(1 + Xvx)
= 1 + Λ1x + Λ2x2
+ ⋯ + Λvxv
0 = 1 + Λ1X−1
j + Λ2X−2
j + ⋯ + ΛvX−v
j
0 = YjXi+v
j + Λ1YjXi+v−1
j + ⋯ + ΛvYjXi
j
0 =
v
∑
j=1
YjXi+v
j +
v
∑
j=1
Λ1YjXi+v−1
j + ⋯ +
v
∑
j=1
ΛvYjXi
j
0 = Si+v + Λ1Si+v−1 + ⋯ + ΛvSi
Multiply YjXi+v
j
Add up for all j
19. Decoder - Error evaluation
• Get the roots of the locator equation, we got the error locations
.
• After substitute the error locations with solved values, here’s coming to the
final calculation: find the error values.
• Recover the codeword with error polynomial and drop the parity symbols. We
get the transmitted message.
• It’s better to go through all the calculation with our simple example. (P.S. the
algorithms for RS(544,514) are much more complex than in the simple
example)
(X1, X2, ⋯, Xv)
Xj
(1 + X1x)(1 + X2x)⋯(1 + Xvx) = 1 + Λ1x + Λ2x2
+ ⋯ + Λvxv
Si = Y1Xi
1 + Y2Xi
2 + ⋯ + YvXi
v
C(x) = R(x) − E(x)
20. Decoder - Example
• First, use received symbols to get the ‘Syndromes’.
[12 12 7 3 11 10 9 8 7 0 5 4 3 2 1]
1 1 1 1
1 2 4 8
1 4 3 12
1 8 12 10
1 3 5 15
1 6 7 1
1 12 15 8
1 11 9 12
1 5 2 10
1 10 8 15
1 7 6 1
1 14 11 8
1 15 10 12
1 13 14 10
1 9 13 15
= [2 10 9 1]
errors
Syndromesreceived codeword
parity check matrix
Here we use the parity check matrix. Divide the codeword
with with do the same thing.g(x)