Subra Ganesan DSP 1.

Similar documents
REAL TIME DIGITAL SIGNAL PROCESSING. Introduction

Appendix B. Design Implementation Description For The Digital Frequency Demodulator

Lesson 7. Digital Signal Processors

Evolution of DSP Processors. Kartik Kariya EE, IIT Bombay

AutoBench 1.1. software benchmark data book.

Problem Point Value Your score Topic 1 28 Filter Analysis 2 24 Filter Implementation 3 24 Filter Design 4 24 Potpourri Total 100

Vector Arithmetic Logic Unit Amit Kumar Dutta JIS College of Engineering, Kalyani, WB, India

SIGMA-DELTA CONVERTER

Control Systems Overview REV II

Digital Signal Processing

Digital Integrated CircuitDesign

You CAN Do Digital Filtering with an MCU!

COURSE PLAN. : DIGITAL SIGNAL PROCESSING : Dr.M.Pallikonda.Rajasekaran, Professor/ECE

Digital Signal Processing. VO Embedded Systems Engineering Armin Wasicek WS 2009/10

Chapter 6: DSP And Its Impact On Technology. Book: Processor Design Systems On Chip. By Jari Nurmi

CHAPTER 4 HARDWARE DEVELOPMENT OF STATCOM

Keywords: Adaptive filtering, LMS algorithm, Noise cancellation, VHDL Design, Signal to noise ratio (SNR), Convergence Speed.

EECS 452 Midterm Exam (solns) Fall 2012

A DSP IMPLEMENTED DIGITAL FM MULTIPLEXING SYSTEM

Performance Analysis of FIR Filter Design Using Reconfigurable Mac Unit

AUTOMATIC IMPLEMENTATION OF FIR FILTERS ON FIELD PROGRAMMABLE GATE ARRAYS

Option 1: A programmable Digital (FIR) Filter

Design of Digital FIR Filter using Modified MAC Unit

GUJARAT TECHNOLOGICAL UNIVERSITY

AREA EFFICIENT DISTRIBUTED ARITHMETIC DISCRETE COSINE TRANSFORM USING MODIFIED WALLACE TREE MULTIPLIER

Implementation of FPGA based Design for Digital Signal Processing

Design Implementation Description for the Digital Frequency Oscillator

A Survey on Power Reduction Techniques in FIR Filter

CHAPTER 2 FIR ARCHITECTURE FOR THE FILTER BANK OF SPEECH PROCESSOR

Exploring DSP Performance

EEM478-WEEK8 Finite Impulse Response (FIR) Filters

The University of Texas at Austin Dept. of Electrical and Computer Engineering Midterm #1

Design of a High Speed FIR Filter on FPGA by Using DA-OBC Algorithm

FIR Filter Design on Chip Using VHDL

Using the TMS320C24X DSP Controller for Optimal Digital Control

EECS 452 Midterm Closed book part Winter 2013

CS3291: Digital Signal Processing

Application Note, V1.0, March 2008 AP XC2000 Family. DSP Examples for C166S V2 Lib. Microcontrollers

The Comparative Study of FPGA based FIR Filter Design Using Optimized Convolution Method and Overlap Save Method

Globally Asynchronous Locally Synchronous (GALS) Microprogrammed Parallel FIR Filter

SCUBA-2. Low Pass Filtering

On-Chip Implementation of Cascaded Integrated Comb filters (CIC) for DSP applications

Digital Hearing Aids Specific μdsp Chip Design by Verilog HDL

Microcomputer Systems 1. Introduction to DSP S

II Year (04 Semester) EE6403 Discrete Time Systems and Signal Processing

y(n)= Aa n u(n)+bu(n) b m sin(2πmt)= b 1 sin(2πt)+b 2 sin(4πt)+b 3 sin(6πt)+ m=1 x(t)= x = 2 ( b b b b

A New RNS 4-moduli Set for the Implementation of FIR Filters. Gayathri Chalivendra

A Low-Power Broad-Bandwidth Noise Cancellation VLSI Circuit Design for In-Ear Headphones

Digital Logic, Algorithms, and Functions for the CEBAF Upgrade LLRF System Hai Dong, Curt Hovater, John Musson, and Tomasz Plawski

SYLLABUS. For B.TECH. PROGRAMME ELECTRONICS & COMMUNICATION ENGINEERING

Mahendra Engineering College, Namakkal, Tamilnadu, India.

DIGITAL FILTERING OF MULTIPLE ANALOG CHANNELS

Final Exam. EE313 Signals and Systems. Fall 1999, Prof. Brian L. Evans, Unique No

AC : INTERACTIVE LEARNING DISCRETE TIME SIGNALS AND SYSTEMS WITH MATLAB AND TI DSK6713 DSP KIT

DSP Filter Design for Flexible Alternating Current Transmission Systems

MULTIRATE IIR LINEAR DIGITAL FILTER DESIGN FOR POWER SYSTEM SUBSTATION

Keyword ( FIR filter, program counter, memory controller, memory modules SRAM & ROM, multiplier, accumulator and stack pointer )

A13C: Performing Digital Filtering on an MCU

Design and FPGA Implementation of High-speed Parallel FIR Filters

Based with permission on lectures by John Getty Laboratory Electronics II (PHSX262) Spring 2011 Lecture 9 Page 1

ELEC-C5230 Digitaalisen signaalinkäsittelyn perusteet

Introduction to Real-Time Digital Signal Processing

Lecture Schedule: Week Date Lecture Title

Lab 4 Digital Scope and Spectrum Analyzer

IMPLEMENTATION OF DIGITAL FILTER ON FPGA FOR ECG SIGNAL PROCESSING

Analogue Interfacing. What is a signal? Continuous vs. Discrete Time. Continuous time signals

Signals and Systems Using MATLAB

Optimized Digital Filtering for the MSP430

Dr. D. M. Akbar Hussain

Microprocessors & Interfacing

Design and Analysis of RNS Based FIR Filter Using Verilog Language

Implementation of CIC filter for DUC/DDC

High Speed IIR Notch Filter Using Pipelined Technique

Team proposals are due tomorrow at 6PM Homework 4 is due next thur. Proposal presentations are next mon in 1311EECS.

DSP Project. Reminder: Project proposal is due Friday, October 19, 2012 by 5pm in my office (Small 239).

DSP-BASED FM STEREO GENERATOR FOR DIGITAL STUDIO -TO - TRANSMITTER LINK

Digital Signal Processing of Speech for the Hearing Impaired

Digital Filters IIR (& Their Corresponding Analog Filters) Week Date Lecture Title

Chapter 2: Digitization of Sound

INTRODUCTION DIGITAL SIGNAL PROCESSING

Time: 3 hours Max Marks: 70 Answer any FIVE questions All questions carry equal marks *****

An area optimized FIR Digital filter using DA Algorithm based on FPGA

Problem Point Value Your score Topic 1 28 Discrete-Time Filter Analysis 2 24 Improving Signal Quality 3 24 Filter Bank Design 4 24 Potpourri Total 100

Generating DTMF Tones Using Z8 Encore! MCU

A New High Speed Low Power Performance of 8- Bit Parallel Multiplier-Accumulator Using Modified Radix-2 Booth Encoded Algorithm

L T P CLASS WORK : EXAM : 100 TOTAL : 150 DURATION OF EXAM : 3 HRS

LLRF4 Evaluation Board

CHAPTER 2 VSI FED INDUCTION MOTOR DRIVE

INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATION ENGINEERING & TECHNOLOGY (IJECET) STUDY ON COMPARISON OF VARIOUS MULTIPLIERS

SINGLE MAC IMPLEMENTATION OF A 32- COEFFICIENT FIR FILTER USING XILINX

Music 270a: Fundamentals of Digital Audio and Discrete-Time Signals

FIR System Specification

Using FPGA. Warin Sootkaneung Department of Electrical Engineering. and

FACULTY OF ENGINEERING AND TECHNOLOGY

Noise removal example. Today s topic. Digital Signal Processing. Lecture 3. Application Specific Integrated Circuits for

Linear Systems. Claudia Feregrino-Uribe & Alicia Morales-Reyes Original material: Rene Cumplido. Autumn 2015, CCC-INAOE

Implementing Logic with the Embedded Array

GSM Interference Cancellation For Forensic Audio

Design of Adjustable Reconfigurable Wireless Single Core

UPSC Electrical Engineering Syllabus

Transcription:

DSP 1 Subra Ganesan Professor, Computer Science and Engineering Associate Director, Product Development and Manufacturing Center, Oakland University, Rochester, MI 48309 Email: ganesan@oakland.edu

Topics Covered: 1. Introduction to DSP Processors 2. Fixed Point DSP- c24x 3. Floating Point DSP- C6711 4. Code Composer Studio 5. DSP/BIOS for C6711 6. External Memory Interface for C6711 7. Interrupt C6711 8. Applications

DSP Microprocessor Advances and Automotive Applications Advances in Circuit Technology, Architecture, Algorithms and VLSI design techniques have contributed to high performance Digital Signal Processing(DSP) microprocessors and to multitude of novel applications of DSP chips. DSP processors are RISC based which have fast arithmetic units, on chip memory, analog interface, serial ports, timers, counters, facilities for inter processor communications and other special features.

The Microprocessor overview 1949 Transistors 1958 Integrated Circuits 1961 ICs IN Quality 1964 Small Scale IC(SSI) Gates 1968 Medium Scale IC(MSI) Registers 1971 Large Scale IC(LSI), Memory, CPU 1972 8 BIT MICROPROCESSORS 1973 16 BIT MICROPROCESSORS 1982 32 BIT MICROPROCESSORS 1984 DSP MICROPROCESSORS I GENERATION 1986 DSP MICROPROCESSORS II GENERATION 1988 DSP MICROPROCESSORS III GENERATION 1989 RISC MICROPROCESSORS II QUALITY 1990 MISC MINIMUM INSTRUSTION SET MICROPROCESSOR

MICROPROCESSOR OVERVIEW Microprocessor Number of transistors Performance Number of Instructions 4 Bit Intel 4004 2300 45 1971 68000 70000 0.5 MIPS 80 Different 14 address mode Size B,W,L TMS 320C80 32 bit RISC 2 Billion operations per second [BOPs]

INTRODUCTION TO DSP MICROPROCESSORS DSP micros are reduced-instruction-set computers optimized for the fastest possible execution of the following instructions Addition Subtraction Multiplication Shifting Single cycle multiplication and shifting using ARRAY multiplier and barrel (or combination) shifter. In contrast, general purpose micros effect such as operations via multiple cycle, micro-code instructions that make use of the ALU s single cycle, parallel-add, single bit shift capability.

DSP micros do each multiply/accumulate in a single cycle = (e.g 100 ns.) For 80386: Add( 16 bit addition) = 125 ns (16 Mhz) (IMUL) 16 bit * 16 bit multiplication = 1250 ns

DSP micros employ Pipe lining of instructions Use of addressing modes that efficiently access relevant data structure (e.g., auto increment, auto decrement modes for arrays & Indexed addressing modes for FFTs)

Dual-Bus HARVARD ARCHITECTURE, which enables Simultaneous fetching of data and instructions Special DSP related addressing modes (e.g., Index computation module an arbitrary number, automatic circular queue or free data move for FIR filters, bit reversal for FFTs) Extra addressing,multiple ALUs Special interfaces to serve specific fields of application( e.g., serial interfaces for CODEC in telecommunications)

Progress in new technologies, Gallium arsenate (GaAs) transistors and high electron-mobility transmission increase in the future DSP microprocessor. 80836 computes 1024 point FFT only 66% slower than 20 MHz TMS 32010. New version general purpose micros with DSP like dual bus structures(e.g., 68030 Motorola) array multiplier, barrel shifter, GaAs/HEMT technology, can achieve a performance of 100 MIPS and upwards. TMS 32010 does = 5 MIPs 320C25 = 10 MIPs Motorola 56000 = 10.25 MIPs(24 bit data) TMS 320 C 6201 = 1600 MIPs

FLOATING-POINT DIGITAL SIGNAL PROCEESING CHIPS DSP has the capability to perform floating-point arithmetic including multiply-accumulate operations with an increased degree of parallelism. The design phase is often performed with the aid of high-level language or a commercial, DSP-oriented design system that yields a nonreal-time, floating point simulation on a general purpose computer. The new generation of floating point digital signal processors are AT&T, DSP32C, Motorola DSP96002, and Texas Instruments TMS320C30.

A typical development system could involve an Iconic graphical interface( implemented in PC software) A computer A PC plugin board containing a floating point DSP micro chip Memory system

The Next PC is the first to incorporate a DSP micro. The on-board Motorola fixed-point DSP56001 is complemented by numerous canned procedures. These procedures enable graphics and signal processing tasks to be carried out at rates orders-of-magnitude faster than possible with on-board MC68882 floating-point co-processor. The cycle of improvement in functionality and performance for both general-purpose and DSP micros continues. Architectures incorporating such structures as systolic arrays and neural networks, will replace those now considered conventional.

DSP APPLICATIONS CHARACTERSTICS 1. Algorithms are mathematically intensive e.g., for FIR filter Where n-1 y(n) = a(i) * x(n-1) i=0 y(n) = output samples a(i) = coefficients x(n-1) = input samples 2. Real time performance e.g. Speech Recognition Image processing within a frame update period

3. Sample Input Signal DSP processor must effectively handle sampled data in large quantities. DSP processors must be flexible to accommodate changing algorithms, new DSP processors etc.

The DSP Environment: Definitions Analog Signal Lowpass Filter (LPF1) A/D Converter DSP Processor D/A Converter Lowpass Filter (LPF2) Analog Signal

A simple digital filter system A/D fs a(0) X(n) X(n) Sample Register, R R X(n-1) X(n-2) X X X X a(1) a(2) R X(n-N+1) Where fs sampling frequency a(0),a(i) co-efficients y(n) Digital output y(t) analog output + Y(n) D/A Y(t)

As long as the system samples the analog input at a frequency f s that is at least twice the information band width of that input, all information present in the original analog signal is contained in the digital signal A/D conversion introduces quantization noise. Signal to quantization noise ratio or SQNR is a function of A/D s accuracy. DSP stores current A/D sample and N-1 previous samples in a sample shift register, or a RAM which can simulate shift register function by modifying memory address pointers. The coefficients a i are stored in ROM or RAM and they determine the impulse response and filter characteristics. A large N gives a longer impulse response and generally produces filters with sharper roll-off, greater stop band attenuation, and less frequency ripple.

This filter is called Nth order, finite impulse response (FIR) (no feed back path), digital filter. The FIR filter requires N multiplies and N-1 additions to compute an output y(n) each time the input signal is sampled. Some DSP applications involve sampling rates of up to 100 Mhz and 100 MIPS.

SHANNON S SAMPLING THEORY An analog signal containing maximum frequency f i Hz may be completely represented by regularly spaced samples, provided the sampling rate is at least 2f 1 samples per second. f s = 2f 1 Nyquist sampling rate. If sampled at less than 2f 1 rate, aliasing error occurs. Signal is then represented with distortion which depends on the degree of aliasing. Use anti-aliasing filter, a low-pas filter with cut-off frequency at f 1 (or f s /2)

Quantization Noise (Q e ) a(t) A/D n bit Q e = ± ( V ref / 2 * 2 n ) e.g. V ref = 5 V, n = 8 then Q e = 5 / 512

G(f) (a) Input spectrum f (a) Input continuous time signal g(t) f s /2 (b) Sampled spectrum f SAMP (b) Sampled signal g r (t) f s /2 f SAMP (c) Reconstructed spectrum Fig. Aliasing in the frequency domain (c) Reconstructed signal Fig. Aliasing in the time domain

LINEAR SYSTEM obeys the principle of superposition. If an input consisting of a number of signals is applied to a linear system, then the output is the sum or the superposition of the system s responses to each signal considered separately

FREQUENCY PRESERVATION PROPERTY If we apply a complicated signal containing many frequencies, the output must be the sum of output due to each input frequency, considered separately. The output contains only those frequencies present in the input. TIME INVARIANT SYSTEM It is the one whose property do not vary with time.

LTI: Linear Time Invariant LTI associative property means that we may analyze a complicated LTI system by breaking down into a number of simpler subsystems. Commutative Property It means that the subsystems can be arranged in series or cascaded in any order without affecting the overall performance.

Causal System In this system the output depends only on the present and or/previous values of the input. Stable System It is one that produces a finite or bounded output in response to the bounded input.

Invertibility If a system with input x[n] gives an output y[n], then its inverse would produce x[n] if fed with y[n].

BIT REVERSED ADDRESSING It is a special type of indirect addressing. It is used for implementing FFT *ARn ++ (IRO)B After the operand is fetched, AR n is updated to (AR N + IRO) in a reversed carry propagation format.

CIRCULAR ADDRESSING A circular buffer is necessary to implement the delays associated with convolution and correlation equations. The block size is in register Bk. *ARI ++; ARI is incremented each time until it points to the bottom of the circular buffer. After that it will point to the top of the buffer.

REPEAT INSTRUCTION A block of instruction is repeated count number of times using RPTB. RC contains the count number. LDI 8, RC RPTB Label 1 CALL filter FIX RO Label1 STI RO, * AR3 RPTB instruction repeats next instruction count number of times

PARALLEL INSTRUCTION The symbol indicated parallel operation LDF 0, RO LDI 29, AR2 RPTS AR2 MPYF *ARO++, *AR1++, R0 ADDF RO, R2, R2 New Value Old value MPYF ---> Multiply Floating point number Parallel operation

DELAYED BRANCH Conditional or unconditional delayed branch allows the subsequent 3 instruction to be fetched and executed. This gives the effect of single cycle branch. BD Loop; Delayed Branch ADDF R0, R1 } FIX R1 } executed whether STI R1, *AR3 } branch is taken or not Loop Standard branches empty the pipeline before branching. This results in taking 4 cycles to execute branch.

DSP CHIPS Analog Devices ADSP 2100, 21020 AT&T DSP 16. 32 DSP semiconductors Pine 16 bit fixed point Motorola 56100, 96000 NEC upd 77C25 (16 bit fixed pt) 77220 (24 bit fixed pt) SGS Thomson ST 18 ( 16 bit fixed point) Start semiconductor SPROC 1000 24 bit fixed point Texas Instruments TMS3201x, 2x, 3x, 4x, 80, 6xx Zilog Z89 Cxx 16 bit fixed DSP Xilinx DSP FPGA

MARKET SHARE TI 46.7% AT&T 18.7% MOTOROLA 15% AD 9.3% NEC 8.4% OTHER 1.9%

DSP Vs Microcontroller Microcontroller Digital Signal Processor Multicycle instruction set. Single cycle inst. set. Multicycle multiplicity. Single cycle multiply. 8 or 16 bit support. 16/32 bit fixed or floating. Limited onchip RAM. Large on chip data RAM. Limited data pointers. Data pointers. Limited BW and limited algorithms. Speed!