Post on 26-Dec-2015
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 11
ELEC 7770ELEC 7770Advanced VLSI DesignAdvanced VLSI Design
Spring 2010Spring 2010Constraint Graph and Retiming SolutionConstraint Graph and Retiming Solution
Vishwani D. AgrawalVishwani D. AgrawalJames J. Danaher ProfessorJames J. Danaher Professor
ECE Department, Auburn UniversityECE Department, Auburn University
Auburn, AL 36849Auburn, AL 36849
vagrawal@eng.auburn.eduhttp://www.eng.auburn.edu/~vagrawal/COURSE/E7770_Spr10/course.htmlhttp://www.eng.auburn.edu/~vagrawal/COURSE/E7770_Spr10/course.html
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 22
Retiming TheoremRetiming Theorem Given a network G(V, E, W) and a cycle time T, Given a network G(V, E, W) and a cycle time T,
(r1, . . . ) is a feasible retiming if and only if:(r1, . . . ) is a feasible retiming if and only if: ri – rj ri – rj ≤ wij≤ wij for all edges (vi,vj) for all edges (vi,vj) εε E E ri – rj ≤ W(vi,vj) – 1 ri – rj ≤ W(vi,vj) – 1 for all node-pairs vi, vj such thatfor all node-pairs vi, vj such that
D(vi,vj) D(vi,vj) > T> T
Where,Where,
W(vi,vj) is the minimum weight path between vi and vjW(vi,vj) is the minimum weight path between vi and vj
D(vi,vj) is the maximum delay among all minimum D(vi,vj) is the maximum delay among all minimum weight paths between vi and vjweight paths between vi and vj
Retiming Theorem ExplainedRetiming Theorem Explained Condition 1, ri – rj Condition 1, ri – rj ≤ wij is related to edge weight:≤ wij is related to edge weight:
Original circuit is feasible => original weight wij is positiveOriginal circuit is feasible => original weight wij is positive Originally, ri = rj = 0Originally, ri = rj = 0 Retiming, rj flip-flops added to eij, ri flip-flops removed Retiming, rj flip-flops added to eij, ri flip-flops removed
from eij, net reduction ri – rj must be less than wij to leave from eij, net reduction ri – rj must be less than wij to leave the retimed weight of eij positive.the retimed weight of eij positive.
Condition 2, ri – rj ≤ W(vi,vj) – 1 is related to path Condition 2, ri – rj ≤ W(vi,vj) – 1 is related to path delays between node pairs being less than clock delays between node pairs being less than clock period T whenever path weight is 0.period T whenever path weight is 0.
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 33
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 44
Timing OptimizationTiming Optimization
Find the clock period (T) by path analysis.Find the clock period (T) by path analysis. Set clock period to T/2 and find a feasible Set clock period to T/2 and find a feasible
retiming.retiming. If feasible, further reduce the clock period to If feasible, further reduce the clock period to
half.half. If not feasible, increase clock period.If not feasible, increase clock period. Do a binary search for optimum clock period.Do a binary search for optimum clock period. Retime the circuit.Retime the circuit.
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 55
Representing a ConstraintRepresenting a Constraint
ri – rj ≤ wij or rj ≥ ri – wij
rj ri– wij
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 66
Constraint GraphConstraint Graph
r1 ≥ r0 + 3 r1 ≥ r2 + 1 r2 ≥ r0 + 1 r2 ≥ r1 – 1 r3 ≥ r1 + 1 r3 ≥ r2 + 4 r0 ≥ r3 – 6 r0
r1
r2
r3-1 1
3 1
1 4
-6
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 77
Feasibility ConditionFeasibility Condition
A set of values for variables can be found if and A set of values for variables can be found if and only if the constraint graph has no positive only if the constraint graph has no positive cycles.cycles.
This is also the condition for the solvability of the This is also the condition for the solvability of the longest path problem, which provides a solution longest path problem, which provides a solution to the set of constraints.to the set of constraints.
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 88
Example: Infeasible ConstraintsExample: Infeasible Constraints
x1 ≥ x2 + 6 x2 ≥ x1 – 3
x1 x2
6
-3
x1
x2
60x1 ≥ x2 + 6
x2 ≥ x1 – 3
3
3
Positive cycle mean no longest path can be found.
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 99
Solving a Constraint SetSolving a Constraint Set
r1 ≥ r0 + 3 r1 ≥ r2 + 1 r2 ≥ r0 + 1 r2 ≥ r1 – 1 r3 ≥ r1 + 1 r3 ≥ r2 + 4 r0 ≥ r3 – 6 r0
r1
r2
r3-1 1
3 1
1 4
-6
Longest paths from source r0 to r0, r1, r2, r3Path lengths: s0=0, s1=3, s2=2, s3=6Solution: r0=0, r1=3, r2=2, r3=6
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1010
The General Path ProblemThe General Path Problem Find the shortest (or longest) path in a graph Find the shortest (or longest) path in a graph
from a source vertex to all other vertices.from a source vertex to all other vertices. Graph has vertices and directed edges:Graph has vertices and directed edges:
Edge weights can be positive or negativeEdge weights can be positive or negative Graph can be cyclicGraph can be cyclic Single source vertex – a vertex with 0 in-degree (not Single source vertex – a vertex with 0 in-degree (not
a necessary condition)a necessary condition)
Inconsistent problemsInconsistent problems Negative weight cycles for shortest pathNegative weight cycles for shortest path Positive weight cycles for longest pathPositive weight cycles for longest path
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1111
Dijkstra’s Shortest Path AlgorithmDijkstra’s Shortest Path Algorithm
Greedy algorithm.Greedy algorithm. Applies to directed acyclic graphs (DAG) with Applies to directed acyclic graphs (DAG) with positivepositive
edge weights.edge weights. Computational complexityComputational complexity
O(|E| + |V| log |V|) O(|E| + |V| log |V|) ≤ O(n≤ O(n22)) References:References:
A. Aho, J. Hopcroft and J. Ullman, A. Aho, J. Hopcroft and J. Ullman, Data Structures and Data Structures and AlgorithmsAlgorithms, Reading, Massachusetts: Addison-Wesley, 1983., Reading, Massachusetts: Addison-Wesley, 1983.
T. Cormen, C. Leiserson and R. Rivest, T. Cormen, C. Leiserson and R. Rivest, Introduction to Introduction to AlgorithmsAlgorithms, New York: McGraw-Hill, 1990., New York: McGraw-Hill, 1990.
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1212
Dijkstra’s Shortest Path Algorithm Dijkstra’s Shortest Path Algorithm Example 1Example 1
v0
v2
v3
v1w01=15 3
10
2 6source
si = path weight (v0, vi)
Alg. stepsAlg. steps s0s0 s1s1 s2s2 s3s3
Initially: mark v0Initially: mark v0 00 1515 22
Step 1: mark v2Step 1: mark v2 00 1212 22 88
Step 2: mark v3Step 2: mark v3 00 1111 22 88
Step 3: mark v1Step 3: mark v1 00 1111 22 88
Each step marks the path with smallest weight and updates the unmarked path weights.
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1313
Dijkstra’s Shortest Path Algorithm Dijkstra’s Shortest Path Algorithm Example 2Example 2
v0
v2
v3
v1w01=15 3
102
6
source
si = path weight (v0, vi)
Alg. stepsAlg. steps s0s0 s1s1 s2s2 s3s3
Initially: mark v0Initially: mark v0 00 1515 22
Step 1: mark v2Step 1: mark v2 00 88 22 1212
Step 2: mark v1Step 2: mark v1 00 88 22 1212
Step 3: mark v3Step 3: mark v3 00 88 22 1212
Each step marks the path with smallest weight and updates the unmarked path weights.
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1414
Dijkstra’s Algorithm, G(V, E, W)Dijkstra’s Algorithm, G(V, E, W)
s0(1) = 0s0(1) = 0 initialize sourceinitialize source
for ( i = 1 to n )for ( i = 1 to n ) initialize path weights, n=|V| –1initialize path weights, n=|V| –1si(1) = w0isi(1) = w0i
repeat {repeat {
Select an unmarked vertex vq such that sq is minimalSelect an unmarked vertex vq such that sq is minimal
Mark vqMark vq
foreach ( unmarked vertex vi )foreach ( unmarked vertex vi )si =si = min min { si, sq + wqi } { si, sq + wqi }
}}until (all vertices are marked)until (all vertices are marked)
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1515
Try Dijkstra’s Algorithm for Your GraphTry Dijkstra’s Algorithm for Your Graph
http://www.dgp.toronto.edu/people/JamesStewart/270/9798s/Laffra/DijkstraApplet.html
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1616
Dijkstra’s Longest Path AlgorithmDijkstra’s Longest Path Algorithm
v0
v2
v3
v1w01=15 3
10
2 6source
si = path length (v0, vi)
Alg. stepsAlg. steps s0s0 s1s1 s2s2 s3s3
InitiallyInitially 00 -15-15 -2-2
Step 1: mark v1Step 1: mark v1 00 -15-15 -2-2
Step 2: mark v2Step 2: mark v2 00 -15-15 -2-2 -8-8
Step 3: mark v3Step 3: mark v3 00 -15-15 -2-2 -8-8
v0
v2
v3
v1w01= -15 -3
-10
-2 -6source
Either change min to maxOr change all positive weights to negatives
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1717
Dijkstra’s Alg. Does Not Work for Dijkstra’s Alg. Does Not Work for Cycles, Mixed WeightsCycles, Mixed Weights
v0
v2
v3
v1w01=15 3
5
2 4source
si = path weight (v0, vi)
Alg. stepsAlg. steps s0s0 s1s1 s2s2 s3s3
Initially: mark v0Initially: mark v0 00 1515 22
Step 1: mark v2Step 1: mark v2 00 77 22 66
Step 2: mark v3Step 2: mark v3 00 77 22 66
Step 3: mark v1Step 3: mark v1 00 77 22 6?6?
-2
Algorithm stops because all vertices are marked.But, there exists a v0 to v3 path of length 5
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1818
Bellman’s Equations – Shortest PathBellman’s Equations – Shortest Path
vi
vn
vm
vkvj
sq = minimum path weight betweensource and vq
wki
wji
wmi
wni
For all vertices:
si = min (sq + wqi)
vq ε pred(vi)
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 1919
Bellman-Ford Algorithm, G(V, E, W)Bellman-Ford Algorithm, G(V, E, W)Bellman-Ford {Bellman-Ford {
s0(1) = 0s0(1) = 0 initialize sourceinitialize source
for ( i = 1 to n )for ( i = 1 to n ) initialize path weights, n = |V| – 1initialize path weights, n = |V| – 1si(1) = w0isi(1) = w0i
for ( j = 1 to n )for ( j = 1 to n ) n iterationsn iterationsfor ( i = 1 to n )for ( i = 1 to n )
si(j+1) =si(j+1) = min min { si(j), sk(j) + wkj } { si(j), sk(j) + wkj }
vvk k εε pred(vi) pred(vi)
}}
if ( si(j+1) == si(j) if ( si(j+1) == si(j) i ) return (true)i ) return (true)
}}
return (false)return (false) Complexity = O(|V||E|) ≤ O(n3)
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2020
Bellman-Ford Shortest PathBellman-Ford Shortest Path
v0
v2
v3
v1w01=15 3
10
2 6source
si = path weight (v0, vi)
Alg. stepsAlg. steps s0s0 s1s1 s2s2 s3s3
InitiallyInitially 00 1515 22
Iteration 1Iteration 1 00 1212 22 88
Iteration 2Iteration 2 00 1111 22 88
Iteration 3Iteration 3 00 1111 22 88
n = 3
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2121
Bellman-Ford Longest PathBellman-Ford Longest Path
v0
v2
v3
v1w01= -15 -3
-10
-2 -6source
si = path weight (v0, vi)
Alg. stepsAlg. steps s0s0 s1s1 s2s2 s3s3
InitiallyInitially 00 -15-15 -2-2
Iteration 1Iteration 1 00 -15-15 -2-2 -8-8
Iteration 2Iteration 2 00 -15-15 -2-2 -8-8
n = 3 (shortest path)
Reverse the sign of weights and solve shortest path problem.(Alternative: keep original weights and change min operator in algorithm to max.)
Weights reversed
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2222
Bellman’s Equations – Longest PathBellman’s Equations – Longest Path
vi
vn
vm
vkvj
sq = maximum path weight betweensource and vq
wki
wji
wmi
wni
For all vertices:
si = max (sq + wqi)
vq ε pred(vi)
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2323
Bellman-Ford for Cycles, Neg. WeightsBellman-Ford for Cycles, Neg. Weights
v0
v2
v3
v1w01=15 3
5
2 4source
si = path weight (v0, vi)
Alg. stepsAlg. steps s0s0 s1s1 s2s2 s3s3
InitiallyInitially 00 1515 22
Iteration 1Iteration 1 00 77 22 66
Iteration 2Iteration 2 00 77 22 55
Iteration 3Iteration 3 00 77 22 55
-2 n = 3 (shortest path)
This was incorrect with Dijkstra’s shortest path algorithm
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2424
Bellman-Ford for Negative CycleBellman-Ford for Negative Cycle
v0
v2
v3
v1w01=15 -3
5
2 4source
si = path weight (v0, vi)
Alg. stepsAlg. steps s0s0 s1s1 s2s2 s3s3
InitiallyInitially 00 1515 22
Iteration 1Iteration 1 00 77 22 66
Iteration 2Iteration 2 00 33 22 66
Iteration 3Iteration 3 00 33 22 55
2
Values not stabilized after n iterations.Inconsistent problem: negative cycle.
n = 3 (shortest path)
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2525
Retiming ExampleRetiming Example
FF10 5 5
Delay
a b c
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2626
Retiming GraphRetiming Graph
FF10 5 5a b c
h0
a10
b5
c5
0 0 1
1
Critical path = 15It is the longest path consisting only of zero weight edges.
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2727
Feasibility Constraints (Condition 1)Feasibility Constraints (Condition 1)
FF10 5 5a b c
h0
a10
b5
c5
0 0 1
1
ri – rj ≤ wij edges i → jRetiming should not cause negative edge weights.
rh – ra ≤ 0ra – rb ≤ 0rb – rc ≤ 1rc – rh ≤ 1
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2828
Constraint GraphConstraint Graph
FF10 5 5a b c
rh0
ra10
rb5
rc5
0 0 -1
-1
ri – rj ≤ wij edges i → jRetiming should not cause negative edge weights.
rh – ra ≤ 0ra – rb ≤ 0 Constraints forrb – rc ≤ 1 Condition 1rc – rh ≤ 1
Observation: Constraint graph has the same structure as the original retiming graph, with signs of weights reversed. Vertex labels are the retiming integer variables.
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 2929
Max Delay for Min Weight PathsMax Delay for Min Weight Paths
h0
a10
b5
c5
0 0 1
1
W(h,a) = 0 D(h,a) = 10W(h,b) = 0 D(h,b) = 15W(h,c) = 1 D(h,c) = 20W(a,b) = 0 D(a,b) = 15W(a,c) = 1 D(a,c) = 20W(a,h) = 2 D(a,h) = 20
W(b,c) = 1 D(b,c) = 10W(b,h) = 2 D(b,h) = 10W(b,a) = 2 D(b,a) = 20W(c,h) = 1 D(c,h) = 5W(c,a) = 1 D(c,a) = 15W(c,b) = 1 D(c,b) = 20
T = 15
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 3030
Timing Optimization, T = 7.5?Timing Optimization, T = 7.5?
W(h,a) = 0 D(h,a) = 10W(h,b) = 0 D(h,b) = 15W(h,c) = 1 D(h,c) = 20W(a,b) = 0 D(a,b) = 15W(a,c) = 1 D(a,c) = 20W(a,h) = 2 D(a,h) = 20
W(b,c) = 1 D(b,c) = 10W(b,h) = 2 D(b,h) = 10W(b,a) = 2 D(b,a) = 20W(c,h) = 1 D(c,h) = 5W(c,a) = 1 D(c,a) = 15W(c,b) = 1 D(c,b) = 20
rh0
ra10
rb5
rc5
0 0 -1
-1
Add constraints forCondition 2: ri – rj ≤ W(I,j) – 1 paths (i,j) such that D(i,j) > 7.5
Constraint graph(feasibility)
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 3131
Timing Optimization, T = 7.5?Timing Optimization, T = 7.5?
W(h,a) = 0 D(h,a) = 10W(h,b) = 0 D(h,b) = 15W(h,c) = 1 D(h,c) = 20W(a,b) = 0 D(a,b) = 15W(a,c) = 1 D(a,c) = 20W(a,h) = 2 D(a,h) = 20
W(b,c) = 1 D(b,c) = 10W(b,h) = 2 D(b,h) = 10W(b,a) = 2 D(b,a) = 20W(c,h) = 1 D(c,h) = 5W(c,a) = 1 D(c,a) = 15W(c,b) = 1 D(c,b) = 20
rh0
ra10
rb5
rc5
0 0 -1
-1
11
0
1
0
-1 0
-1
-1
0
0
Positive cycle;no solution for longest path
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 3232
Timing Optimization, T = 11.25?Timing Optimization, T = 11.25?
W(h,a) = 0 D(h,a) = 10W(h,b) = 0 D(h,b) = 15W(h,c) = 1 D(h,c) = 20W(a,b) = 0 D(a,b) = 15W(a,c) = 1 D(a,c) = 20W(a,h) = 2 D(a,h) = 20
W(b,c) = 1 D(b,c) = 10W(b,h) = 2 D(b,h) = 10W(b,a) = 2 D(b,a) = 20W(c,h) = 1 D(c,h) = 5W(c,a) = 1 D(c,a) = 15W(c,b) = 1 D(c,b) = 20
rh0
ra10
rb5
rc5
0 0 -1
-1
10
1
0
-1 -1
0
0
rh = 0 rb = 1 rc = 0 ra = 0
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 3333
Retiming GraphRetiming Graph
FF10 5 5a b c
h0
a10
b5
c5
0 0 1
1
rh = 0 ra = 0 rb = 1 rc = 0
1 0
wij_retimed = wij + rj – ri
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 3434
Retimed CircuitRetimed Circuit
FF10 5 5a b c
h0
a10
b5
c5
0
1
rh = 0 ra = 0 rb = 1 rc = 0
1 0
Critical Path = 10
Logic optimization will remove these.
ReferenceReference
G. De Micheli, G. De Micheli, Synthesis and Optimization Synthesis and Optimization of Digital Circuitsof Digital Circuits, New York: McGraw-Hill, , New York: McGraw-Hill, 1994.1994.
Spring 2010, Feb 10 . . .Spring 2010, Feb 10 . . . ELEC 7770: Advanced VLSI Design (Agrawal)ELEC 7770: Advanced VLSI Design (Agrawal) 3535