Digital Signal Processing (DSP) Fundamentals: Architecture, Functionalities, and Applicati, Summaries of Bioinformatics

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UNIT – V

Syllabus

DSP functionalities – circular buffering –

DSP architecture – Fixed and Floating

point architecture principles –

Programming – Application examples.

DSP functionalities

• Digital Signal Processors (DSP) take real-world signals like

voice, audio, video, temperature, pressure, or position that

have been digitized and then mathematically manipulate them.

A DSP is designed for performing mathematical functions like

"add", "subtract", "multiply" and "divide" very quickly.

• Signals need to be processed so that the information that they

contain can be displayed, analyzed, or converted to another

type of signal that may be of use. In the real-world, analog

products detect signals such as sound, light, temperature or

pressure and manipulate them. Converters such as an Analog-

to-Digital converter then take the real-world signal and turn it

into the digital format of 1's and 0's. From here, the DSP takes

over by capturing the digitized information and processing it. It

then feeds the digitized information back for use in the real

world. It does this in one of two ways, either digitally or in an

analog format by going through a Digital-to-Analog converter.

All of this occurs at very high speeds.

• To illustrate this concept, the diagram below shows how a DSP

is used in an MP3 audio player. During the recording phase,

analog audio is input through a receiver or other source. This

analog signal is then converted to a digital signal by an analog-

to-digital converter and passed to the DSP. The DSP performs

the MP3 encoding and saves the file to memory. During the

playback phase, the file is taken from memory, decoded by the

DSP and then converted back to an analog signal through the

digital-to-analog converter so it can be output through the

speaker system. In a more complex example, the DSP would

perform other functions such as volume control, equalization

and user interface.

• A DSP's information can be used by a computer to control

such things as security, telephone, home theater systems, and

video compression. Signals may be compressed so that they

can be transmitted quickly and more efficiently from one place

to another (e.g. teleconferencing can transmit speech and video

via telephone lines). Signals may also be enhanced or

manipulated to improve their quality or provide information

that is not sensed by humans (e.g. echo cancellation for cell

phones or computer-enhanced medical images). Although real-

world signals can be processed in their analog form, processing

signals digitally provides the advantages of high speed and

accuracy

RCET

circular buffering

RCET

circular buffering

Digital Signal Processors are designed to quickly carry out FIR filters and

similar techniques. To understand the hardware, we must first understand the

algorithms. In this section we will make a detailed list of the steps needed to

implement an FIR filter. In the next section we will see how DSPs are designed

to perform these steps as efficiently as possible.

To start, we need to distinguish between off-line processing and real-time

processing. In off-line processing, the entire input signal resides in the computer

at the same time. For example, a geophysicist might use a seismometer to

record the ground movement during an earthquake. After the shaking is over,

the information may be read into a computer and analyzed in some way.

Another example of off-line processing is medical imaging, such as computed

tomography and MRI. The data set is acquired while the patient is inside the

machine, but the image reconstruction may be delayed until a later time. The

key point is that all of the information is simultaneously available to the

processing program. This is common in scientific research and engineering, but

not in consumer products. Off-line processing is the realm of personal

computers and mainframes.

In real-time processing, the output signal is produced at the same time that the

input signal is being acquired. For example, this is needed in telephone

communication, hearing aids, and radar. These applications must have the

information immediately available, although it can be delayed by a short

amount. For instance, a 10 millisecond delay in a telephone call cannot be

detected by the speaker or listener. Likewise, it makes no difference if a radar

signal is delayed by a few seconds before being displayed to the operator. Real-

time applications input a sample, perform the algorithm, and output a sample,

over-and-over.

DSP architecture

RCET

Fixed and Floating point architecture principles

Digital signal processing can be separated into two categories - fixed point and

floating point. These designations refer to the format used to store and

manipulate numeric representations of data. Fixed-point DSPs are designed to

represent and manipulate integers – positive and negative whole numbers – via

a minimum of 16 bits, yielding up to 65,536 possible bit patterns (216).

Floating-point DSPs represent and manipulate rational numbers via a minimum

of 32 bits in a manner similar to scientific notation, where a number is

represented with a mantissa and an exponent (e.g., A x 2B, where 'A' is the

mantissa and ‘B’ is the exponent), yielding up to 4,294,967,296 possible bit

patterns (232).

The term ‘fixed point’ refers to the corresponding manner in which numbers are

represented, with a fixed number of digits after, and sometimes before, the

decimal point. With floating-point representation, the placement of the decimal

point can ‘float’ relative to the significant digits of the number. For example, a

fixed-point representation with a uniform decimal point placement convention

can represent the numbers 123.45, 1234.56, 12345.67, etc, whereas a floating-

point representation could in addition represent 1.234567, 123456.7,

0.00001234567, 1234567000000000, etc. As such, floating point can support a

much wider range of values than fixed point, with the ability to represent very

small numbers and very large numbers.

Digital Signal Processing can be divided into two categories, fixed point and

floating point. These refer to the format used to store and manipulate numbers

within the devices. Fixed point DSPs usually represent each number with a

minimum of 16 bits, although a different length can be used. For instance,

Motorola manufactures a family of fixed point DSPs that use 24 bits. There

are four common ways that these 216 = 65536 possible bit patterns can

represent a number. In unsigned integer, the stored number can take on any

integer value from 0 to 65,535. Similarly, signed integer uses two's

complement to make the range include negative numbers, from - 32,768 to

32,767. With unsigned fraction notation, the 65,536 levels are spread

uniformly between 0 and 1. Lastly, the signed fraction format allows negative

numbers, equally spaced between - 1 and 1.

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Fixed point Architecture

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Programs

• Estimate how long will the following code fragment take to

execute on

• A general purpose processor with 1 GHz

operating frequency,five-stage pipelining and 5

cycles required for multiplication, 1 cycle for

addition

• A DSP running at 500 MHz, zero overhead looping

and 6 independent ALUs and 2 independent single-

cycle MAC units?

for (i=0; i<8; i++)

a[i] = 2*i + 3;

b[i] = 3*i + 5;

RCET

• Signal processing in

 Reconfigurable/Cognitive Radar

 Smart Internet of Things

 Cloud and Service Computing

 Smart TV

 3D TV

 4K-TV and UHD TV

• Global IoT Security Solution for

DSP Forecast. Healthcare,

Information Technology (IT),

Telecom, Banking, Financial

Services, And Insurance (BFSI),

Automotive & Others.