SlideShare a Scribd company logo
1 of 12
Design and Implementation of VLSI Systems
                   (EN1600)
                      Lecture 13: Logical Effort (2/2)




S. Reda EN160 SP’08
Multistage logic networks
       • Logical effort generalizes to multistage networks

       • Path Logical Effort            G = ∏ gi
                                               Cout-path
       • Path Electrical Effort         H=
                                               Cin-path
       • Path Effort                    F = ∏ f i = ∏ gi hi
                 10
                               x                       z
                                           y
                                                                  20
                 g1 = 1      g2 = 5/3   g3 = 4/3      g4 = 1
                 h1 = x/10   h2 = y/x   h3 = z/y      h4 = 20/z

          Can we write F=GH?
S. Reda EN160 SP’08
Can we write F = GH?

    •     No! Consider paths that branch:

          G       =1                        15
                                                 90
          H       = 90 / 5 = 18
                                        5
          GH      = 18
          h1      = (15 +15) / 5 = 6        15
                                                 90
          h2      = 90 / 15 = 6
          F       = g1g2h1h2 = 36 = 2GH
         How to fix this problem?



S. Reda EN160 SP’08
Branching effort

      • Introduce branching effort
            – Accounts for branching between stages in path
                      Con path + Coff path
                b=
                           Con path
                B = ∏ bi
                                                Note:

                                                ∏h      i   = BH
      • Now we compute the path effort
            – F = GBH



S. Reda EN160 SP’08
Logical Effort can help us answering two key
   questions

   1. How large should be each stage in a multi-
      stage network to achieve the minimium delay?
   2. What is the optimal number of stages to
      achieve the minimum delay




S. Reda EN160 SP’08
1. What is the optimal size of each stage?

                                              Gate      Gate
                                               1         2



                                                               GND




   Delay is minimized when each stage bears the same effort

    Answer can be generalized. Thus, for N stages, minimum delay
    is achieved when each stage bears the same effort



S. Reda EN160 SP’08
Example: 3-stage path

      • Select gate sizes x and y for least delay from
        A to B
                          x

                                      y
                          x
                                                 45
              A       8
                          x
                                      y      B
                                                 45




S. Reda EN160 SP’08
Example: 3-stage path
                      x

                            y
                      x
                                     45
         A   8
                      x
                            y    B
                                     45



             Logical Effort               G=
             Electrical Effort            H=
             Branching Effort             B=
             Path Effort                  F=
             Best Stage Effort            ˆ
                                          f =
             Parasitic Delay              P=
             Delay                        D=

S. Reda EN160 SP’08
Example: 3-stage path
                      x

                            y
                      x
                                     45
         A   8
                      x
                            y    B
                                     45



             Logical Effort               G = (4/3)*(5/3)*(5/3) = 100/27
             Electrical Effort            H = 45/8
             Branching Effort             B=3*2=6
             Path Effort                  F = GBH = 125
             Best Stage Effort            ˆ
                                          f = 3 F =5
             Parasitic Delay              P=2+3+2=7
             Delay                        D = 3*5 + 7 = 22 = 4.4 FO4

S. Reda EN160 SP’08
Example: 3-stage path

      •     Work backward for sizes
            y = 45 * (5/3) / 5 = 15
            x = (15*2) * (5/3) / 5 = 10




                                                      45
              A P: 4
                               P: 4
                N: 4                      P: 12   B
                               N: 6                   45
                                          N: 3



S. Reda EN160 SP’08
2. What is the optimal number of stages?

      • Consider adding inverters to end of path
            – How many give least delay?      Logic Block:
                                                              N - n1 ExtraInverters

                                               n1Stages
                           n1                 Path Effort F

          D = NF + ∑ pi + ( N − n1 ) pinv
                      1
                      N


                           i =1
          ∂D      1    1   1
             = − F ln F + F + pinv = 0
                  N    N   N

          ∂N
                                                 1
           Define best stage effort ρ = F        N



                  pinv + ρ ( 1 − ln ρ ) = 0


S. Reda EN160 SP’08
Optimal number of stages
    •      pinv + ρ ( 1 − ln ρ ) = 0 has no closed-form solution
    • Neglecting parasitics (pinv = 0), we find r = 2.718 (e)
    • For pinv = 1, solve numerically for r = 3.59
    • A path achieves least delay by using               stages
    • How sensitive is delay to using exactly the best number
      of stages?                               1.6
                                                     1.51



                                  D(N) /D(N)
    • ρ = 4 is reasonable                      1.4

                                               1.2                        1.15
                                                                                 1.26


                                               1.0

                                                                  (ρ=6)           (ρ =2.4)




                                               0.0
                                                            0.5   0.7     1.0      1.4       2.0

                                                                          N/ N

S. Reda EN160 SP’08

More Related Content

What's hot

Lec 5 asymptotic notations and recurrences
Lec 5 asymptotic notations and recurrencesLec 5 asymptotic notations and recurrences
Lec 5 asymptotic notations and recurrencesAnkita Karia
 
High-dimensional polytopes defined by oracles: algorithms, computations and a...
High-dimensional polytopes defined by oracles: algorithms, computations and a...High-dimensional polytopes defined by oracles: algorithms, computations and a...
High-dimensional polytopes defined by oracles: algorithms, computations and a...Vissarion Fisikopoulos
 
Asymptotic notations
Asymptotic notationsAsymptotic notations
Asymptotic notationsEhtisham Ali
 
Analysis of algorithn class 3
Analysis of algorithn class 3Analysis of algorithn class 3
Analysis of algorithn class 3Kumar
 
Linear-Size Approximations to the Vietoris-Rips Filtration - Presented at Uni...
Linear-Size Approximations to the Vietoris-Rips Filtration - Presented at Uni...Linear-Size Approximations to the Vietoris-Rips Filtration - Presented at Uni...
Linear-Size Approximations to the Vietoris-Rips Filtration - Presented at Uni...Don Sheehy
 
Mba admission in india
Mba admission in indiaMba admission in india
Mba admission in indiaEdhole.com
 
Polyhedral computations in computational algebraic geometry and optimization
Polyhedral computations in computational algebraic geometry and optimizationPolyhedral computations in computational algebraic geometry and optimization
Polyhedral computations in computational algebraic geometry and optimizationVissarion Fisikopoulos
 
Little o and little omega
Little o and little omegaLittle o and little omega
Little o and little omegaRajesh K Shukla
 
Wk 12 fr bode plot nyquist may 9 2016
Wk 12 fr bode plot nyquist   may 9 2016Wk 12 fr bode plot nyquist   may 9 2016
Wk 12 fr bode plot nyquist may 9 2016Charlton Inao
 
Justesen codes alternant codes goppa codes
Justesen codes alternant codes goppa codesJustesen codes alternant codes goppa codes
Justesen codes alternant codes goppa codesMadhumita Tamhane
 
Multicasting in Linear Deterministic Relay Network by Matrix Completion
Multicasting in Linear Deterministic Relay Network by Matrix CompletionMulticasting in Linear Deterministic Relay Network by Matrix Completion
Multicasting in Linear Deterministic Relay Network by Matrix CompletionTasuku Soma
 
Distorted diffeomorphisms and path connectedness of Z^d actions on 1-manifolds
Distorted diffeomorphisms and path connectedness of Z^d actions on 1-manifoldsDistorted diffeomorphisms and path connectedness of Z^d actions on 1-manifolds
Distorted diffeomorphisms and path connectedness of Z^d actions on 1-manifoldsAndrius Navas
 
Asymptotic notations ada
Asymptotic notations adaAsymptotic notations ada
Asymptotic notations adaShaishavShah8
 

What's hot (20)

Lec 5 asymptotic notations and recurrences
Lec 5 asymptotic notations and recurrencesLec 5 asymptotic notations and recurrences
Lec 5 asymptotic notations and recurrences
 
High-dimensional polytopes defined by oracles: algorithms, computations and a...
High-dimensional polytopes defined by oracles: algorithms, computations and a...High-dimensional polytopes defined by oracles: algorithms, computations and a...
High-dimensional polytopes defined by oracles: algorithms, computations and a...
 
Aho corasick-lecture
Aho corasick-lectureAho corasick-lecture
Aho corasick-lecture
 
Asymptotic notations
Asymptotic notationsAsymptotic notations
Asymptotic notations
 
Analysis of algorithn class 3
Analysis of algorithn class 3Analysis of algorithn class 3
Analysis of algorithn class 3
 
Chap8 new
Chap8 newChap8 new
Chap8 new
 
Approximation Algorithms
Approximation AlgorithmsApproximation Algorithms
Approximation Algorithms
 
Lecture4
Lecture4Lecture4
Lecture4
 
Linear-Size Approximations to the Vietoris-Rips Filtration - Presented at Uni...
Linear-Size Approximations to the Vietoris-Rips Filtration - Presented at Uni...Linear-Size Approximations to the Vietoris-Rips Filtration - Presented at Uni...
Linear-Size Approximations to the Vietoris-Rips Filtration - Presented at Uni...
 
Mba admission in india
Mba admission in indiaMba admission in india
Mba admission in india
 
Polyhedral computations in computational algebraic geometry and optimization
Polyhedral computations in computational algebraic geometry and optimizationPolyhedral computations in computational algebraic geometry and optimization
Polyhedral computations in computational algebraic geometry and optimization
 
Little o and little omega
Little o and little omegaLittle o and little omega
Little o and little omega
 
Wk 12 fr bode plot nyquist may 9 2016
Wk 12 fr bode plot nyquist   may 9 2016Wk 12 fr bode plot nyquist   may 9 2016
Wk 12 fr bode plot nyquist may 9 2016
 
Justesen codes alternant codes goppa codes
Justesen codes alternant codes goppa codesJustesen codes alternant codes goppa codes
Justesen codes alternant codes goppa codes
 
Lecture26
Lecture26Lecture26
Lecture26
 
P1 Bearing Modul
P1 Bearing ModulP1 Bearing Modul
P1 Bearing Modul
 
Multicasting in Linear Deterministic Relay Network by Matrix Completion
Multicasting in Linear Deterministic Relay Network by Matrix CompletionMulticasting in Linear Deterministic Relay Network by Matrix Completion
Multicasting in Linear Deterministic Relay Network by Matrix Completion
 
Distorted diffeomorphisms and path connectedness of Z^d actions on 1-manifolds
Distorted diffeomorphisms and path connectedness of Z^d actions on 1-manifoldsDistorted diffeomorphisms and path connectedness of Z^d actions on 1-manifolds
Distorted diffeomorphisms and path connectedness of Z^d actions on 1-manifolds
 
Asymptotic notations ada
Asymptotic notations adaAsymptotic notations ada
Asymptotic notations ada
 
Bode lect
Bode lectBode lect
Bode lect
 

Similar to Lecture13

Volume and edge skeleton computation in high dimensions
Volume and edge skeleton computation in high dimensionsVolume and edge skeleton computation in high dimensions
Volume and edge skeleton computation in high dimensionsVissarion Fisikopoulos
 
module4_dynamic programming_2022.pdf
module4_dynamic programming_2022.pdfmodule4_dynamic programming_2022.pdf
module4_dynamic programming_2022.pdfShiwani Gupta
 
Characterizing the Distortion of Some Simple Euclidean Embeddings
Characterizing the Distortion of Some Simple Euclidean EmbeddingsCharacterizing the Distortion of Some Simple Euclidean Embeddings
Characterizing the Distortion of Some Simple Euclidean EmbeddingsDon Sheehy
 
module1_Introductiontoalgorithms_2022.pdf
module1_Introductiontoalgorithms_2022.pdfmodule1_Introductiontoalgorithms_2022.pdf
module1_Introductiontoalgorithms_2022.pdfShiwani Gupta
 
Algorithm to count number of disjoint paths
Algorithm to count number of disjoint pathsAlgorithm to count number of disjoint paths
Algorithm to count number of disjoint pathsSujith Jay Nair
 
PAWL - GPU meeting @ Warwick
PAWL - GPU meeting @ WarwickPAWL - GPU meeting @ Warwick
PAWL - GPU meeting @ WarwickPierre Jacob
 
Gate 2013 complete solutions of ec electronics and communication engineering
Gate 2013 complete solutions of ec  electronics and communication engineeringGate 2013 complete solutions of ec  electronics and communication engineering
Gate 2013 complete solutions of ec electronics and communication engineeringmanish katara
 
Chap7 2 Ecc Intro
Chap7 2 Ecc IntroChap7 2 Ecc Intro
Chap7 2 Ecc IntroEdora Aziz
 
Case Study (All)
Case Study (All)Case Study (All)
Case Study (All)gudeyi
 
dynamic programming Rod cutting class
dynamic programming Rod cutting classdynamic programming Rod cutting class
dynamic programming Rod cutting classgiridaroori
 
Differentiation using First Principle - By Mohd Noor Abdul Hamid
Differentiation using First Principle  - By Mohd Noor Abdul HamidDifferentiation using First Principle  - By Mohd Noor Abdul Hamid
Differentiation using First Principle - By Mohd Noor Abdul HamidMohd. Noor Abdul Hamid
 

Similar to Lecture13 (20)

Volume and edge skeleton computation in high dimensions
Volume and edge skeleton computation in high dimensionsVolume and edge skeleton computation in high dimensions
Volume and edge skeleton computation in high dimensions
 
Lecture17
Lecture17Lecture17
Lecture17
 
Ece4510 notes08
Ece4510 notes08Ece4510 notes08
Ece4510 notes08
 
Kaplan turbines
Kaplan turbinesKaplan turbines
Kaplan turbines
 
module4_dynamic programming_2022.pdf
module4_dynamic programming_2022.pdfmodule4_dynamic programming_2022.pdf
module4_dynamic programming_2022.pdf
 
Characterizing the Distortion of Some Simple Euclidean Embeddings
Characterizing the Distortion of Some Simple Euclidean EmbeddingsCharacterizing the Distortion of Some Simple Euclidean Embeddings
Characterizing the Distortion of Some Simple Euclidean Embeddings
 
Madrid easy
Madrid easyMadrid easy
Madrid easy
 
Channel coding
Channel codingChannel coding
Channel coding
 
Information Security Seminar #2
Information Security Seminar #2Information Security Seminar #2
Information Security Seminar #2
 
module1_Introductiontoalgorithms_2022.pdf
module1_Introductiontoalgorithms_2022.pdfmodule1_Introductiontoalgorithms_2022.pdf
module1_Introductiontoalgorithms_2022.pdf
 
Algorithm to count number of disjoint paths
Algorithm to count number of disjoint pathsAlgorithm to count number of disjoint paths
Algorithm to count number of disjoint paths
 
PAWL - GPU meeting @ Warwick
PAWL - GPU meeting @ WarwickPAWL - GPU meeting @ Warwick
PAWL - GPU meeting @ Warwick
 
Ece4510 notes09
Ece4510 notes09Ece4510 notes09
Ece4510 notes09
 
Ec gate'13
Ec gate'13Ec gate'13
Ec gate'13
 
Gate 2013 complete solutions of ec electronics and communication engineering
Gate 2013 complete solutions of ec  electronics and communication engineeringGate 2013 complete solutions of ec  electronics and communication engineering
Gate 2013 complete solutions of ec electronics and communication engineering
 
Convex hull in 3D
Convex hull in 3DConvex hull in 3D
Convex hull in 3D
 
Chap7 2 Ecc Intro
Chap7 2 Ecc IntroChap7 2 Ecc Intro
Chap7 2 Ecc Intro
 
Case Study (All)
Case Study (All)Case Study (All)
Case Study (All)
 
dynamic programming Rod cutting class
dynamic programming Rod cutting classdynamic programming Rod cutting class
dynamic programming Rod cutting class
 
Differentiation using First Principle - By Mohd Noor Abdul Hamid
Differentiation using First Principle  - By Mohd Noor Abdul HamidDifferentiation using First Principle  - By Mohd Noor Abdul Hamid
Differentiation using First Principle - By Mohd Noor Abdul Hamid
 

More from Dharmesh Goyal (20)

What's new in Bluetooth 5 ? Facts Unleashed
What's new in Bluetooth 5 ? Facts UnleashedWhat's new in Bluetooth 5 ? Facts Unleashed
What's new in Bluetooth 5 ? Facts Unleashed
 
Lecture19
Lecture19Lecture19
Lecture19
 
Lecture20
Lecture20Lecture20
Lecture20
 
Lecture32
Lecture32Lecture32
Lecture32
 
Lecture31
Lecture31Lecture31
Lecture31
 
Lecture30
Lecture30Lecture30
Lecture30
 
Lecture29
Lecture29Lecture29
Lecture29
 
Lecture28
Lecture28Lecture28
Lecture28
 
Lecture27
Lecture27Lecture27
Lecture27
 
Lecture26
Lecture26Lecture26
Lecture26
 
Lecture25
Lecture25Lecture25
Lecture25
 
Lecture24
Lecture24Lecture24
Lecture24
 
Lecture23
Lecture23Lecture23
Lecture23
 
Lecture22
Lecture22Lecture22
Lecture22
 
Lecture21
Lecture21Lecture21
Lecture21
 
Lecture32
Lecture32Lecture32
Lecture32
 
Lecture18
Lecture18Lecture18
Lecture18
 
Lecture17
Lecture17Lecture17
Lecture17
 
Lecture16
Lecture16Lecture16
Lecture16
 
Lecture15
Lecture15Lecture15
Lecture15
 

Recently uploaded

Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Sapana Sha
 
Final demo Grade 9 for demo Plan dessert.pptx
Final demo Grade 9 for demo Plan dessert.pptxFinal demo Grade 9 for demo Plan dessert.pptx
Final demo Grade 9 for demo Plan dessert.pptxAvyJaneVismanos
 
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptxPOINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptxSayali Powar
 
History Class XII Ch. 3 Kinship, Caste and Class (1).pptx
History Class XII Ch. 3 Kinship, Caste and Class (1).pptxHistory Class XII Ch. 3 Kinship, Caste and Class (1).pptx
History Class XII Ch. 3 Kinship, Caste and Class (1).pptxsocialsciencegdgrohi
 
How to Configure Email Server in Odoo 17
How to Configure Email Server in Odoo 17How to Configure Email Server in Odoo 17
How to Configure Email Server in Odoo 17Celine George
 
Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)eniolaolutunde
 
Solving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptxSolving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptxOH TEIK BIN
 
CARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptxCARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptxGaneshChakor2
 
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdfssuser54595a
 
Biting mechanism of poisonous snakes.pdf
Biting mechanism of poisonous snakes.pdfBiting mechanism of poisonous snakes.pdf
Biting mechanism of poisonous snakes.pdfadityarao40181
 
Blooming Together_ Growing a Community Garden Worksheet.docx
Blooming Together_ Growing a Community Garden Worksheet.docxBlooming Together_ Growing a Community Garden Worksheet.docx
Blooming Together_ Growing a Community Garden Worksheet.docxUnboundStockton
 
Sanyam Choudhary Chemistry practical.pdf
Sanyam Choudhary Chemistry practical.pdfSanyam Choudhary Chemistry practical.pdf
Sanyam Choudhary Chemistry practical.pdfsanyamsingh5019
 
Incoming and Outgoing Shipments in 1 STEP Using Odoo 17
Incoming and Outgoing Shipments in 1 STEP Using Odoo 17Incoming and Outgoing Shipments in 1 STEP Using Odoo 17
Incoming and Outgoing Shipments in 1 STEP Using Odoo 17Celine George
 
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...Marc Dusseiller Dusjagr
 
Paris 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityParis 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityGeoBlogs
 
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Krashi Coaching
 
Computed Fields and api Depends in the Odoo 17
Computed Fields and api Depends in the Odoo 17Computed Fields and api Depends in the Odoo 17
Computed Fields and api Depends in the Odoo 17Celine George
 
Organic Name Reactions for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions  for the students and aspirants of Chemistry12th.pptxOrganic Name Reactions  for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions for the students and aspirants of Chemistry12th.pptxVS Mahajan Coaching Centre
 

Recently uploaded (20)

Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
 
Final demo Grade 9 for demo Plan dessert.pptx
Final demo Grade 9 for demo Plan dessert.pptxFinal demo Grade 9 for demo Plan dessert.pptx
Final demo Grade 9 for demo Plan dessert.pptx
 
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptxPOINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
POINT- BIOCHEMISTRY SEM 2 ENZYMES UNIT 5.pptx
 
Model Call Girl in Tilak Nagar Delhi reach out to us at 🔝9953056974🔝
Model Call Girl in Tilak Nagar Delhi reach out to us at 🔝9953056974🔝Model Call Girl in Tilak Nagar Delhi reach out to us at 🔝9953056974🔝
Model Call Girl in Tilak Nagar Delhi reach out to us at 🔝9953056974🔝
 
9953330565 Low Rate Call Girls In Rohini Delhi NCR
9953330565 Low Rate Call Girls In Rohini  Delhi NCR9953330565 Low Rate Call Girls In Rohini  Delhi NCR
9953330565 Low Rate Call Girls In Rohini Delhi NCR
 
History Class XII Ch. 3 Kinship, Caste and Class (1).pptx
History Class XII Ch. 3 Kinship, Caste and Class (1).pptxHistory Class XII Ch. 3 Kinship, Caste and Class (1).pptx
History Class XII Ch. 3 Kinship, Caste and Class (1).pptx
 
How to Configure Email Server in Odoo 17
How to Configure Email Server in Odoo 17How to Configure Email Server in Odoo 17
How to Configure Email Server in Odoo 17
 
Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)
 
Solving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptxSolving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptx
 
CARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptxCARE OF CHILD IN INCUBATOR..........pptx
CARE OF CHILD IN INCUBATOR..........pptx
 
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf
18-04-UA_REPORT_MEDIALITERAСY_INDEX-DM_23-1-final-eng.pdf
 
Biting mechanism of poisonous snakes.pdf
Biting mechanism of poisonous snakes.pdfBiting mechanism of poisonous snakes.pdf
Biting mechanism of poisonous snakes.pdf
 
Blooming Together_ Growing a Community Garden Worksheet.docx
Blooming Together_ Growing a Community Garden Worksheet.docxBlooming Together_ Growing a Community Garden Worksheet.docx
Blooming Together_ Growing a Community Garden Worksheet.docx
 
Sanyam Choudhary Chemistry practical.pdf
Sanyam Choudhary Chemistry practical.pdfSanyam Choudhary Chemistry practical.pdf
Sanyam Choudhary Chemistry practical.pdf
 
Incoming and Outgoing Shipments in 1 STEP Using Odoo 17
Incoming and Outgoing Shipments in 1 STEP Using Odoo 17Incoming and Outgoing Shipments in 1 STEP Using Odoo 17
Incoming and Outgoing Shipments in 1 STEP Using Odoo 17
 
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
“Oh GOSH! Reflecting on Hackteria's Collaborative Practices in a Global Do-It...
 
Paris 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityParis 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activity
 
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
Kisan Call Centre - To harness potential of ICT in Agriculture by answer farm...
 
Computed Fields and api Depends in the Odoo 17
Computed Fields and api Depends in the Odoo 17Computed Fields and api Depends in the Odoo 17
Computed Fields and api Depends in the Odoo 17
 
Organic Name Reactions for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions  for the students and aspirants of Chemistry12th.pptxOrganic Name Reactions  for the students and aspirants of Chemistry12th.pptx
Organic Name Reactions for the students and aspirants of Chemistry12th.pptx
 

Lecture13

  • 1. Design and Implementation of VLSI Systems (EN1600) Lecture 13: Logical Effort (2/2) S. Reda EN160 SP’08
  • 2. Multistage logic networks • Logical effort generalizes to multistage networks • Path Logical Effort G = ∏ gi Cout-path • Path Electrical Effort H= Cin-path • Path Effort F = ∏ f i = ∏ gi hi 10 x z y 20 g1 = 1 g2 = 5/3 g3 = 4/3 g4 = 1 h1 = x/10 h2 = y/x h3 = z/y h4 = 20/z Can we write F=GH? S. Reda EN160 SP’08
  • 3. Can we write F = GH? • No! Consider paths that branch: G =1 15 90 H = 90 / 5 = 18 5 GH = 18 h1 = (15 +15) / 5 = 6 15 90 h2 = 90 / 15 = 6 F = g1g2h1h2 = 36 = 2GH How to fix this problem? S. Reda EN160 SP’08
  • 4. Branching effort • Introduce branching effort – Accounts for branching between stages in path Con path + Coff path b= Con path B = ∏ bi Note: ∏h i = BH • Now we compute the path effort – F = GBH S. Reda EN160 SP’08
  • 5. Logical Effort can help us answering two key questions 1. How large should be each stage in a multi- stage network to achieve the minimium delay? 2. What is the optimal number of stages to achieve the minimum delay S. Reda EN160 SP’08
  • 6. 1. What is the optimal size of each stage? Gate Gate 1 2 GND Delay is minimized when each stage bears the same effort Answer can be generalized. Thus, for N stages, minimum delay is achieved when each stage bears the same effort S. Reda EN160 SP’08
  • 7. Example: 3-stage path • Select gate sizes x and y for least delay from A to B x y x 45 A 8 x y B 45 S. Reda EN160 SP’08
  • 8. Example: 3-stage path x y x 45 A 8 x y B 45 Logical Effort G= Electrical Effort H= Branching Effort B= Path Effort F= Best Stage Effort ˆ f = Parasitic Delay P= Delay D= S. Reda EN160 SP’08
  • 9. Example: 3-stage path x y x 45 A 8 x y B 45 Logical Effort G = (4/3)*(5/3)*(5/3) = 100/27 Electrical Effort H = 45/8 Branching Effort B=3*2=6 Path Effort F = GBH = 125 Best Stage Effort ˆ f = 3 F =5 Parasitic Delay P=2+3+2=7 Delay D = 3*5 + 7 = 22 = 4.4 FO4 S. Reda EN160 SP’08
  • 10. Example: 3-stage path • Work backward for sizes y = 45 * (5/3) / 5 = 15 x = (15*2) * (5/3) / 5 = 10 45 A P: 4 P: 4 N: 4 P: 12 B N: 6 45 N: 3 S. Reda EN160 SP’08
  • 11. 2. What is the optimal number of stages? • Consider adding inverters to end of path – How many give least delay? Logic Block: N - n1 ExtraInverters n1Stages n1 Path Effort F D = NF + ∑ pi + ( N − n1 ) pinv 1 N i =1 ∂D 1 1 1 = − F ln F + F + pinv = 0 N N N ∂N 1 Define best stage effort ρ = F N pinv + ρ ( 1 − ln ρ ) = 0 S. Reda EN160 SP’08
  • 12. Optimal number of stages • pinv + ρ ( 1 − ln ρ ) = 0 has no closed-form solution • Neglecting parasitics (pinv = 0), we find r = 2.718 (e) • For pinv = 1, solve numerically for r = 3.59 • A path achieves least delay by using stages • How sensitive is delay to using exactly the best number of stages? 1.6 1.51 D(N) /D(N) • ρ = 4 is reasonable 1.4 1.2 1.15 1.26 1.0 (ρ=6) (ρ =2.4) 0.0 0.5 0.7 1.0 1.4 2.0 N/ N S. Reda EN160 SP’08