digital-electronics_9 encoder and decoder pdfsomanathbtech
Encoders and decoders are fundamental components in various fields such as computer science, telecommunications, and information theory. They play crucial roles in transforming data from one format to another, enabling efficient storage, transmission, and processing of information.
An encoder is a device, algorithm, or process that converts data from one form to another. It takes input data and transforms it into a different representation, often in a more compact or suitable format for a particular application. Encoders are commonly used in digital communication systems, where they prepare data for transmission over channels with specific characteristics or constraints.
In digital communication, encoders are essential for converting analog signals into digital form for processing and transmission. For example, in audio and video compression, encoders convert raw audio or video data into compressed formats like MP3 or MPEG, reducing the amount of data required for storage or transmission without significantly compromising quality.
Similarly, in computer networks, encoders prepare data for transmission over network links by converting it into packets with appropriate headers and formatting. This ensures efficient use of bandwidth and enables reliable communication between devices.
In information theory, encoders play a vital role in coding schemes such as error correction and data compression. Error-correcting codes use encoders to add redundancy to data, allowing receivers to detect and correct errors introduced during transmission. Data compression algorithms use encoders to remove redundancy and minimize the amount of data required to represent information, facilitating efficient storage and transmission.
On the other hand, decoders perform the inverse operation of encoders. They take encoded data as input and convert it back into its original format or representation. Decoders are commonly used in conjunction with encoders to enable reversible transformations and ensure accurate reconstruction of the original data.
In digital communication systems, decoders are responsible for recovering transmitted data from received signals, undoing the encoding process applied by the transmitter. This process is critical for reliable communication, especially in noisy or error-prone environments where data may be corrupted during transmission.
In multimedia applications, decoders are used to decompress encoded audio and video data, reconstructing the original signals for playback or further processing. Decoders for popular compression formats like JPEG, MP3, and H.264 are widely used in devices such as smartphones, computers, and streaming media players.
In information theory, decoders are essential for decoding error-correcting codes and recovering the original data despite errors introduced during transmission. Decoders analyze received data and use error detection and correction techniques to identify and correct errors, ensuring the integrity of
digital-electronics_9 encoder and decoder pdfsomanathbtech
Encoders and decoders are fundamental components in various fields such as computer science, telecommunications, and information theory. They play crucial roles in transforming data from one format to another, enabling efficient storage, transmission, and processing of information.
An encoder is a device, algorithm, or process that converts data from one form to another. It takes input data and transforms it into a different representation, often in a more compact or suitable format for a particular application. Encoders are commonly used in digital communication systems, where they prepare data for transmission over channels with specific characteristics or constraints.
In digital communication, encoders are essential for converting analog signals into digital form for processing and transmission. For example, in audio and video compression, encoders convert raw audio or video data into compressed formats like MP3 or MPEG, reducing the amount of data required for storage or transmission without significantly compromising quality.
Similarly, in computer networks, encoders prepare data for transmission over network links by converting it into packets with appropriate headers and formatting. This ensures efficient use of bandwidth and enables reliable communication between devices.
In information theory, encoders play a vital role in coding schemes such as error correction and data compression. Error-correcting codes use encoders to add redundancy to data, allowing receivers to detect and correct errors introduced during transmission. Data compression algorithms use encoders to remove redundancy and minimize the amount of data required to represent information, facilitating efficient storage and transmission.
On the other hand, decoders perform the inverse operation of encoders. They take encoded data as input and convert it back into its original format or representation. Decoders are commonly used in conjunction with encoders to enable reversible transformations and ensure accurate reconstruction of the original data.
In digital communication systems, decoders are responsible for recovering transmitted data from received signals, undoing the encoding process applied by the transmitter. This process is critical for reliable communication, especially in noisy or error-prone environments where data may be corrupted during transmission.
In multimedia applications, decoders are used to decompress encoded audio and video data, reconstructing the original signals for playback or further processing. Decoders for popular compression formats like JPEG, MP3, and H.264 are widely used in devices such as smartphones, computers, and streaming media players.
In information theory, decoders are essential for decoding error-correcting codes and recovering the original data despite errors introduced during transmission. Decoders analyze received data and use error detection and correction techniques to identify and correct errors, ensuring the integrity of
digital-electronics_9 encoder and decoder pdfsomanathbtech
Encoders and decoders are fundamental components in various fields such as computer science, telecommunications, and information theory. They play crucial roles in transforming data from one format to another, enabling efficient storage, transmission, and processing of information.
An encoder is a device, algorithm, or process that converts data from one form to another. It takes input data and transforms it into a different representation, often in a more compact or suitable format for a particular application. Encoders are commonly used in digital communication systems, where they prepare data for transmission over channels with specific characteristics or constraints.
In digital communication, encoders are essential for converting analog signals into digital form for processing and transmission. For example, in audio and video compression, encoders convert raw audio or video data into compressed formats like MP3 or MPEG, reducing the amount of data required for storage or transmission without significantly compromising quality.
Similarly, in computer networks, encoders prepare data for transmission over network links by converting it into packets with appropriate headers and formatting. This ensures efficient use of bandwidth and enables reliable communication between devices.
In information theory, encoders play a vital role in coding schemes such as error correction and data compression. Error-correcting codes use encoders to add redundancy to data, allowing receivers to detect and correct errors introduced during transmission. Data compression algorithms use encoders to remove redundancy and minimize the amount of data required to represent information, facilitating efficient storage and transmission.
On the other hand, decoders perform the inverse operation of encoders. They take encoded data as input and convert it back into its original format or representation. Decoders are commonly used in conjunction with encoders to enable reversible transformations and ensure accurate reconstruction of the original data.
In digital communication systems, decoders are responsible for recovering transmitted data from received signals, undoing the encoding process applied by the transmitter. This process is critical for reliable communication, especially in noisy or error-prone environments where data may be corrupted during transmission.
In multimedia applications, decoders are used to decompress encoded audio and video data, reconstructing the original signals for playback or further processing. Decoders for popular compression formats like JPEG, MP3, and H.264 are widely used in devices such as smartphones, computers, and streaming media players.
In information theory, decoders are essential for decoding error-correcting codes and recovering the original data despite errors introduced during transmission. Decoders analyze received data and use error detection and correction techniques to identify and correct errors, ensuring the integrity of
digital-electronics_9 encoder and decoder pdfsomanathbtech
Encoders and decoders are fundamental components in various fields such as computer science, telecommunications, and information theory. They play crucial roles in transforming data from one format to another, enabling efficient storage, transmission, and processing of information.
An encoder is a device, algorithm, or process that converts data from one form to another. It takes input data and transforms it into a different representation, often in a more compact or suitable format for a particular application. Encoders are commonly used in digital communication systems, where they prepare data for transmission over channels with specific characteristics or constraints.
In digital communication, encoders are essential for converting analog signals into digital form for processing and transmission. For example, in audio and video compression, encoders convert raw audio or video data into compressed formats like MP3 or MPEG, reducing the amount of data required for storage or transmission without significantly compromising quality.
Similarly, in computer networks, encoders prepare data for transmission over network links by converting it into packets with appropriate headers and formatting. This ensures efficient use of bandwidth and enables reliable communication between devices.
In information theory, encoders play a vital role in coding schemes such as error correction and data compression. Error-correcting codes use encoders to add redundancy to data, allowing receivers to detect and correct errors introduced during transmission. Data compression algorithms use encoders to remove redundancy and minimize the amount of data required to represent information, facilitating efficient storage and transmission.
On the other hand, decoders perform the inverse operation of encoders. They take encoded data as input and convert it back into its original format or representation. Decoders are commonly used in conjunction with encoders to enable reversible transformations and ensure accurate reconstruction of the original data.
In digital communication systems, decoders are responsible for recovering transmitted data from received signals, undoing the encoding process applied by the transmitter. This process is critical for reliable communication, especially in noisy or error-prone environments where data may be corrupted during transmission.
In multimedia applications, decoders are used to decompress encoded audio and video data, reconstructing the original signals for playback or further processing. Decoders for popular compression formats like JPEG, MP3, and H.264 are widely used in devices such as smartphones, computers, and streaming media players.
In information theory, decoders are essential for decoding error-correcting codes and recovering the original data despite errors introduced during transmission. Decoders analyze received data and use error detection and correction techniques to identify and correct errors, ensuring the integrity of
Convolution codes - Coding/Decoding Tree codes and Trellis codes for multiple...Madhumita Tamhane
In contrast to block codes, Convolution coding scheme has an information frame together with previous m information frames encoded into a single code word frame, hence coupling successive code word frames. Convolution codes are most important Tree codes that satisfy certain additional linearity and time invariance properties. Decoding procedure is mainly devoted to correcting errors in first frame. The effect of these information symbols on subsequent code word frames can be computed and subtracted from subsequent code word frames. Hence in spite of infinitely long code words, computations can be arranged so that the effect of earlier frames, properly decoded, on the current frame is zero.
Convolution codes - Coding/Decoding Tree codes and Trellis codes for multiple...Madhumita Tamhane
In contrast to block codes, Convolution coding scheme has an information frame together with previous m information frames encoded into a single code word frame, hence coupling successive code word frames. Convolution codes are most important Tree codes that satisfy certain additional linearity and time invariance properties. Decoding procedure is mainly devoted to correcting errors in first frame. The effect of these information symbols on subsequent code word frames can be computed and subtracted from subsequent code word frames. Hence in spite of infinitely long code words, computations can be arranged so that the effect of earlier frames, properly decoded, on the current frame is zero.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Water scarcity is the lack of fresh water resources to meet the standard water demand. There are two type of water scarcity. One is physical. The other is economic water scarcity.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
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Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
3. Chapter 4 3
Functions and Functional Blocks
The functions considered are those found to be
very useful in design
Corresponding to each of the functions is a
combinational circuit implementation called a
functional block.
In the past, many functional blocks were
implemented as SSI, MSI, and LSI circuits.
Today, they are often simply parts within a
VLSI circuits.
4. Chapter 4 4
Rudimentary Logic Functions
Functions of a single variable X
Can be used on the
inputs to functional
blocks to implement
other than the block’s
intended function
0
1
F 5 0
F 5 1
(a)
F 5 0
F 5 1
VCC or VDD
(b)
X F 5 X
(c)
X F 5 X
(d)
TABLE 4-1
Functions ofOne Variable
X F = 0 F = X F = F = 1
0
1
0
0
0
1
1
0
1
1
X
5. Chapter 4 5
Multiple-bit Rudimentary Functions
Multi-bit Examples:
A wide line is used to represent
a bus which is a vector signal
In (b) of the example, F = (F3, F2, F1, F0) is a bus.
The bus can be split into individual bits as shown in (b)
Sets of bits can be split from the bus as shown in (c)
for bits 2 and 1 of F.
The sets of bits need not be continuous as shown in (d) for bits 3, 1, and
0 of F.
F
(d)
0
F3
1 F2
F1
A F0
(a)
0
1
A
1
2
3
4
F
0
(b)
4 2:1 F(2:1)
2
F
(c)
4 3,1:0 F(3), F(1:0)
3
A A
6. Chapter 4 6
Enabling Function
Enabling permits an input signal to pass
through to an output
Disabling blocks an input signal from passing
through to an output, replacing it with a fixed
value
The value on the output when it is disable can
be Hi-Z (as for three-state buffers and
transmission gates), 0 , or 1
When disabled, 0 output
When disabled, 1 output
See Enabling App in text
X
F
EN
(a)
EN
X
F
(b)
7. Chapter 4 7
Decoding - the conversion of an n-bit input
code to an m-bit output code with
n m 2n such that each valid code word
produces a unique output code
Circuits that perform decoding are called
decoders
Here, functional blocks for decoding are
• called n-to-m line decoders, where m 2n, and
• generate 2n (or fewer) minterms for the n input
variables
Decoding
8. Chapter 4 8
1-to-2-Line Decoder
2-to-4-Line Decoder
Note that the 2-4-line
made up of 2 1-to-2-
line decoders and 4 AND gates.
Decoder Examples
A D0 D1
0 1 0
1 0 1
(a) (b)
D1 5 A
A
D0 5 A
A1
0
0
1
1
A0
0
1
0
1
D0
1
0
0
0
D1
0
1
0
0
D2
0
0
1
0
D3
0
0
0
1
(a)
D0 5 A1 A0
D1 5 A1 A0
D2 5 A1 A0
D3 5 A1 A0
(b)
A 1
A 0
9. Chapter 4 9
Decoder Expansion
General procedure given in book for any decoder with n
inputs and 2n outputs.
This procedure builds a decoder backward from the
outputs.
The output AND gates are driven by two decoders with
their numbers of inputs either equal or differing by 1.
These decoders are then designed using the same
procedure until 2-to-1-line decoders are reached.
The procedure can be modified to apply to decoders
with the number of outputs ≠ 2n
10. Chapter 4 10
Decoder Expansion - Example 1
3-to-8-line decoder
• Number of output ANDs = 8
• Number of inputs to decoders driving output ANDs = 3
• Closest possible split to equal
2-to-4-line decoder
1-to-2-line decoder
• 2-to-4-line decoder
Number of output ANDs = 4
Number of inputs to decoders driving output ANDs = 2
Closest possible split to equal
• Two 1-to-2-line decoders
See next slide for result
12. Chapter 4 12
Decoder Expansion - Example 2
7-to-128-line decoder
• Number of output ANDs = 128
• Number of inputs to decoders driving output ANDs
= 7
• Closest possible split to equal
4-to-16-line decoder
3-to-8-line decoder
• 4-to-16-line decoder
Number of output ANDs = 16
Number of inputs to decoders driving output ANDs = 2
Closest possible split to equal
• 2 2-to-4-line decoders
• Complete using known 3-8 and 2-to-4 line decoders
13. Chapter 4 13
In general, attach m-enabling circuits to the outputs
See truth table below for function
• Note use of X’s to denote both 0 and 1
• Combination containing two X’s represent four binary combinations
Alternatively, can be viewed as distributing value of signal
EN to 1 of 4 outputs
In this case, called a
demultiplexer
EN
A 1
A 0
D0
D1
D2
D3
(b)
EN A1 A0 D0 D1 D2 D3
0
1
1
1
1
X
0
0
1
1
X
0
1
0
1
0
1
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
1
(a)
Decoder with Enable
14. Chapter 4 14
Encoding
Encoding - the opposite of decoding - the conversion
of an m-bit input code to a n-bit output code with n
m 2n such that each valid code word produces a
unique output code
Circuits that perform encoding are called encoders
An encoder has 2n (or fewer) input lines and n output
lines which generate the binary code corresponding
to the input values
Typically, an encoder converts a code containing
exactly one bit that is 1 to a binary code corres-
ponding to the position in which the 1 appears.
15. Chapter 4 15
Encoder Example
A decimal-to-BCD encoder
• Inputs: 10 bits corresponding to decimal
digits 0 through 9, (D0, …, D9)
• Outputs: 4 bits with BCD codes
• Function: If input bit Di is a 1, then the
output (A3, A2, A1, A0) is the BCD code for i,
The truth table could be formed, but
alternatively, the equations for each of the
four outputs can be obtained directly.
16. Chapter 4 16
Encoder Example (continued)
Input Di is a term in equation Aj if bit Aj is 1
in the binary value for i.
Equations:
A3 = D8 + D9
A2 = D4 + D5 + D6 + D7
A1 = D2 + D3 + D6 + D7
A0 = D1 + D3 + D5 + D7 + D9
F1 = D6 + D7 can be extracted from A2 and A1
Is there any cost saving?
17. Chapter 4 17
Priority Encoder
If more than one input value is 1, then the
encoder just designed does not work.
One encoder that can accept all possible
combinations of input values and produce
a meaningful result is a priority encoder.
Among the 1s that appear, it selects the
most significant input position (or the
least significant input position) containing
a 1 and responds with the corresponding
binary code for that position.
18. Chapter 4 18
Priority Encoder Example
Priority encoder with 5 inputs (D4, D3, D2, D1, D0) - highest priority to
most significant 1 present - Code outputs A2, A1, A0 and V where V
indicates at least one 1 present.
Xs in input part of table represent 0 or 1; thus table entries correspond to
product terms instead of minterms. The column on the left shows that all
32 minterms are present in the product terms in the table
No. of Min-
terms/Row
Inputs Outputs
D4 D3 D2 D1 D0 A2 A1 A0 V
1 0 0 0 0 0 X X X 0
1 0 0 0 0 1 0 0 0 1
2 0 0 0 1 X 0 0 1 1
4 0 0 1 X X 0 1 0 1
8 0 1 X X X 0 1 1 1
16 1 X X X X 1 0 0 1
19. Chapter 4 19
Priority Encoder Example (continued)
Could use a K-map to get equations, but
can be read directly from table and
manually optimized if careful:
A2 = D4
A1 = D3 + D2 = F1, F1 = (D3 + D2)
A0 = D3 + D1 = (D3 + D1)
V = D4 + F1 + D1 + D0
D4 D3
D4 D4
D4 D3
D4 D2 D4 D2
20. Chapter 4 20
Selecting of data or information is a critical
function in digital systems and computers
Circuits that perform selecting have:
• A set of information inputs from which the selection
is made
• A single output
• A set of control lines for making the selection
Logic circuits that perform selecting are called
multiplexers
Selecting can also be done by three-state logic
or transmission gates
Selecting
21. Chapter 4 21
Multiplexers
A multiplexer selects information from an
input line and directs the information to
an output line
A typical multiplexer has n control inputs
(Sn - 1, … S0) called selection inputs, 2n
information inputs (I2
n
- 1, … I0), and one
output Y
A multiplexer can be designed to have m
information inputs with m <2n as well as
n selection inputs
22. Chapter 4 22
2-to-1-Line Multiplexer
Since 2 = 21, n = 1
The single selection variable S has two values:
• S = 0 selects input I0
• S = 1 selects input I1
The equation:
Y = I0 + SI1
The circuit:
S
S
I0
I1
Decoder
Enabling
Circuits
Y
23. Chapter 4 23
2-to-1-Line Multiplexer (continued)
Note the regions of the multiplexer circuit shown:
• 1-to-2-line Decoder
• 2 Enabling circuits
• 2-input OR gate
To obtain a basis for multiplexer expansion, we combine
the Enabling circuits and OR gate into a 2 2 AND-OR
circuit:
• 1-to-2-line decoder
• 2 2 AND-OR
In general, for an 2n-to-1-line multiplexer:
• n-to-2n-line decoder
• 2n 2 AND-OR
25. Chapter 4 25
Multiplexer Width Expansion
Select “vectors of bits” instead of “bits”
Use multiple copies of 2n 2 AND-OR in
parallel
Example:
4-to-1-line
quad multi-
plexer
26. Chapter 4 26
Other Selection Implementations
Three-state logic in place of AND-OR
Gate input cost = 14 compared to 22 (or
18) for gate implementation
I0
I1
I2
I3
S1
S0
(b)
Y
27. Chapter 4 27
Other Selection Implementations
Transmission Gate Multiplexer
Gate input
cost = 8
compared
to 14 for
3-state logic
and 18 or 22
for gate logic
S0
S1
I0
I1
I2
I3
Y
TG
(S0 5 0)
TG
(S1 5 0)
TG
(S1 5 1)
TG
(S0 5 1)
TG
(S0 5 0)
TG
(S0 5 1)
28. Chapter 4 28
Combinational Function Implementation
Alternative implementation techniques:
• Decoders and OR gates
• Multiplexers (and inverter)
• ROMs
• PLAs
• PALs
• Lookup Tables
Can be referred to as structured implementation
methods since a specific underlying structure is
assumed in each case
29. Chapter 4 29
Decoder and OR Gates
Implement m functions of n variables with:
• Sum-of-minterms expressions
• One n-to-2n-line decoder
• m OR gates, one for each output
Approach 1:
• Find the truth table for the functions
• Make a connection to the corresponding OR from
the corresponding decoder output wherever a 1
appears in the truth table
Approach 2
• Find the minterms for each output function
• OR the minterms together
30. Chapter 4 30
Decoder and OR Gates Example
Implement the following set of odd parity functions of
(A7, A6, A5, A3)
P1 = A7 A5 A3
P2 = A7 A6 A3
P4 = A7 A6 A5
Finding sum of
minterms expressions
P1 = Sm(1,2,5,6,8,11,12,15)
P2 = Sm(1,3,4,6,8,10,13,15)
P4 = Sm(2,3,4,5,8,9,14,15)
Find circuit
Is this a good idea?
+
+
+
+
+
+
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
A7
A6
A5
A4
P1
P4
P2
31. Chapter 4 31
Multiplexer Approach 1
Implement m functions of n variables with:
• Sum-of-minterms expressions
• An m-wide 2n-to-1-line multiplexer
Design:
• Find the truth table for the functions.
• In the order they appear in the truth table:
Apply the function input variables to the multiplexer
inputs Sn - 1, … , S0
Label the outputs of the multiplexer with the output
variables
• Value-fix the information inputs to the multiplexer
using the values from the truth table (for don’t cares,
apply either 0 or 1)
32. Chapter 4 32
Example: Gray to Binary Code
Design a circuit to
convert a 3-bit Gray
code to a binary code
The formulation gives
the truth table on the
right
It is obvious from this
table that X = C and the
Y and Z are more complex
Gray
A B C
Binary
x y z
0 0 0 0 0 0
1 0 0 0 0 1
1 1 0 0 1 0
0 1 0 0 1 1
0 1 1 1 0 0
1 1 1 1 0 1
1 0 1 1 1 0
0 0 1 1 1 1
33. Chapter 4 33
Gray to Binary (continued)
Rearrange the table so
that the input combinations
are in counting order
Functions y and z can
be implemented using
a dual 8-to-1-line
multiplexer by:
• connecting A, B, and C to the multiplexer select inputs
• placing y and z on the two multiplexer outputs
• connecting their respective truth table values to the inputs
Gray
A B C
Binary
x y z
0 0 0 0 0 0
0 0 1 1 1 1
0 1 0 0 1 1
0 1 1 1 0 0
1 0 0 0 0 1
1 0 1 1 1 0
1 1 0 0 1 0
1 1 1 1 0 1
34. Chapter 4 34
Note that the multiplexer with fixed inputs is identical to a
ROM with 3-bit addresses and 2-bit data!
Gray to Binary (continued)
D04
D05
D06
D07
S1
S0
A
B
S2
D03
D02
D01
D00
Out
C
D14
D15
D16
D17
S1
S0
A
B
S2
D13
D12
D11
D10
Out
C
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
Y Z
8-to-1
MUX
8-to-1
MUX
35. Chapter 4 35
Multiplexer Approach 2
Implement any m functions of n + 1 variables by using:
• An m-wide 2n-to-1-line multiplexer
• A single inverter
Design:
• Find the truth table for the functions.
• Based on the values of the first n variables, separate the truth
table rows into pairs
• For each pair and output, define a rudimentary function of the
final variable (0, 1, X, )
• Using the first n variables as the index, value-fix the
information inputs to the multiplexer with the corresponding
rudimentary functions
• Use the inverter to generate the rudimentary function
X
X
36. Chapter 4 36
Example: Gray to Binary Code
Design a circuit to
convert a 3-bit Gray
code to a binary code
The formulation gives
the truth table on the
right
It is obvious from this
table that X = C and the
Y and Z are more complex
Gray
A B C
Binary
x y z
0 0 0 0 0 0
1 0 0 0 0 1
1 1 0 0 1 0
0 1 0 0 1 1
0 1 1 1 0 0
1 1 1 1 0 1
1 0 1 1 1 0
0 0 1 1 1 1
37. Chapter 4 37
Gray to Binary (continued)
Rearrange the table so that the input combinations are in
counting order, pair rows, and find rudimentary functions
Gray
A B C
Binary
x y z
Rudimentary
Functions of
C for y
Rudimentary
Functions of
C for z
0 0 0 0 0 0
0 0 1 1 1 1
0 1 0 0 1 1
0 1 1 1 0 0
1 0 0 0 0 1
1 0 1 1 1 0
1 1 0 0 1 0
1 1 1 1 0 1
F = C
F = C
F = C
F = C
F = C
F = C
F = C
F = C
38. Chapter 4 38
Assign the variables and functions to the multiplexer inputs:
Note that this approach (Approach 2) reduces the cost by
almost half compared to Approach 1.
This result is no longer ROM-like
Extending, a function of more than n variables is decomposed
into several sub-functions defined on a subset of the variables.
The multiplexer then selects among these sub-functions.
Gray to Binary (continued)
S1
S0
A
B
D03
D02
D01
D00
Out Y
8-to-1
MUX
C
C
C
C D13
D12
D11
D10
Out Z
8-to-1
MUX
S1
S0
A
B
C
C
C
C
C C
39. Chapter 4 39
Read Only Memory
Functions are implemented by storing the truth
table
Other representations such as equations more
convenient
Generation of programming information from
equations usually done by software
Text Example 4-10 Issue
• Two outputs are generated outside of the ROM
• In the implementation of the system, these two
functions are “hardwired” and even if the ROM is
reprogrammable or removable, cannot be corrected
or updated
40. Chapter 4 40
Programmable Array Logic
There is no sharing of AND gates as in the
ROM and PLA
Design requires fitting functions within
the limited number of ANDs per OR gate
Single function optimization is the first
step to fitting
Otherwise, if the number of terms in a
function is greater than the number of
ANDs per OR gate, then factoring is
necessary
41. Chapter 4 41
Product
term
AND Inputs
Outputs
A B C D W
1
2
3
W = C
4
5
6
F1 = X = A
+ B + W
7
8
9
10
11
12
—
—
—
—
—
—
—
—
—
—
—
—
—
—
—
AB
C
+ ABC
F2 = Y
= AB + BC +AC
BC
A
1
0
—
0
1
—
0
0
—
—
—
—
—
—
1
— —
0
1
0
1
1
1
—
—
—
—
—
—
—
1
—
1
1
1
—
—
1
1
—
—
—
—
—
—
Equations: F1 = A + B + C + ABC
F2 = AB + BC + AC
F1 must be
factored
since four
terms
Factor out
last two
terms as W
A B
B C C A
Programmable Array Logic Example
42. Chapter 4 42
Programmable Array Logic Example
X
X
X
X
X
X
X
X X
X
X X X
X
X X
X X X
AND gates inputs
A C W
Product
term
1
2
3
4
5
6
7
8
9
10
11
12
A
B
C
D
W
F1
F2
All fuses intact
(always 5 0)
X Fuse intact
X
A B B C D D W
A C W
A B B C D D W
1 Fuse blown
43. Chapter 4 43
Programmable Logic Array
The set of functions to be implemented must fit the
available number of product terms
The number of literals per term is less important in
fitting
The best approach to fitting is multiple-output, two-
level optimization (which has not been discussed)
Since output inversion is available, terms can
implement either a function or its complement
For small circuits, K-maps can be used to visualize
product term sharing and use of complements
For larger circuits, software is used to do the
optimization including use of complemented functions
44. Chapter 4 44
Programmable Logic Array Example
K-map
specification
How can this
be implemented
with four terms?
Complete the
programming table
Outputs
1
2
3
4
F2
1
1
–
1
AB
AC
BC
Inputs
–
1
1
C
1
1
–
A
1
–
1
B
PLA programming table
(T)
F1
( )
Product
term
F1 5 A BC + A B C + A B C
F1 5 AB + AC + BC + A B C
0
C
0
1
0 1
0 0
00 01 11 10
BC
A
0
B
1
1
A
0
C
0
1 0
1 1
00 01 11 10
BC
A
1
B
0
1
A
F2 5 AB + AC + BC
F2 5 AC + AB + BC
0
45. Chapter 4 45
Programmable Logic Array Example
X Fuse intact
1 Fuse blown
0
1
F1
F2
A
B
C
C B A
C B A
1
2
4
3
X X
X X
X X
X X
X
X
X
X X
X
X
X
X
X
46. Chapter 4 46
Lookup Tables
Lookup tables are used for implementing logic
in Field-Programmable Gate Arrays (FPGAs)
and Complex Logic Devices (CPLDs)
Lookup tables are typically small, often with
four inputs, one output, and 16 entries
Since lookup tables store truth tables, it is
possible to implement any 4-input function
Thus, the design problem is how to optimally
decompose a set of given functions into a set of
4-input two- level functions.
We will illustrate this by a manual attempt
47. Chapter 4 47
Lookup Table Example
Equations to be implemented:
F1(A,B,C,D,E) = A D E + B D E + C D E
F2(A,B,D,E,F) = A E D + B D E + F D E
Extract 4-input function:
F3(A,B,D,E) = A D E + B D E
F1(C,D,E,F3) = F3 + C D E
F2(D,E,F,F3) = F3 + F D E
The cost of the solution is 3 lookup tables