1. Power Estimation
Dr. Elwin Chandra Monie

2. Abstraction, Complexity, Accuracy
Abstraction level
Computing resources
Analysis accuracy
Algorithm
Least
Worst
Software and system
Hardware behavior
Register transfer
Logic
Circuit
Device
Most
Best

3. Spice Simulation Circuit/device level analysis
Circuit modeled as network of transistors, capacitors, resistors and voltage/current sources.
Node current equations using Kirchhoff’s current law.
Average and instantaneous power computed from supply voltage and device current.
Analysis is accurate but expensive
Used to characterize parts of a larger circuit.

4. Gate-Level Power Analysis
Pre-simulation analysis:
Partition circuit into channel connected gate components.
Determine node capacitances from layout analysis (accurate) or from wire-load model* (approximate).
Determine dynamic and static power from Spice for each gate.
Determine gate delays using Spice or Elmore delay model.
* Wire-load model estimates capacitance of a net by its pin-count.
See Yeap, p. 39.

5. Gate-Level Power Analysis (Cont.)
Run discrete-event (event-driven) logic simulation with a set of input vectors.
Monitor the toggle count of each net and obtain capacitive power dissipation:
Pcap = Σ Ck V 2 f
all nodes k
Where:
Ck is the total node capacitance being switched, as determined by the simulator.
V is the supply voltage.
f is the clock frequency, i.e., the number of vectors applied per unit time

6. Gate-Level Power Analysis (Cont.)
Monitor dynamic energy events at the input of each gate and obtain internal switching power dissipation:
Pint = Σ Σ E(g,e) F(g,e)
gates g events e
Where
E(g,e) = energy of event e of gate g, pre-computed from Spice.
F(g,e) = occurrence frequency of the event e at gate g, observed by logic simulation.

7. Gate-Level Power Analysis (Cont.)
Monitor the static power dissipation state of each gate and obtain the static power dissipation:
Pstat = Σ Σ P(g,s) T(g,s)/ T
gates g states s
Where
P(g,s) = static power dissipation of gate g for state s, obtained from Spice.
T(g,s) = duration of state s at gate g, obtained from logic simulation.
T = vector period.

8. Gate-Level Power Analysis
Sum up all three components of power:
P = Pcap + Pint + Pstat

9. Switching Frequency Number of transitions per unit time: N(t) T = ───
For a continuous signal:
T = lim ───
t→∞ t
T is defined as transition density.

10. Static Signal Probabilities
Observe signal for interval t 0 + t 1
Signal is 1 for duration t 1
Signal is 0 for duration t 0
Signal probabilities:
p 1 = t 1/(t 0 + t 1)
p 0 = t 0/(t 0 + t 1) = 1 – p 1

11. Static Transition Probabilities
T 01 = p 0 Prob{signal is 1 | signal was 0} = p 0 p1
T 10 = p 1 Prob{signal is 0 | signal was 1} = p 1 p 0
T = T 01 + T 10 = 2 p 0 p 1 = 2 p 1 (1 – p 1)
Transition density: T = 2 p 1 (1 – p 1)

12. Static Transition Frequency
0.25
0.2
0.1
0.0
f = p1(1 – p1) p1

13. Inaccuracy in Transition Density
p1 = 0.5
T = 1.0
1/fck
p1 = 0.5
T = 4/6
p1 = 0.5
T = 1/6
Observe that the formula, T = 2 p1 (1 – p1), is not correct.

14. Inaccuracy in Transition Density
p1 = 0.5
T = 1.0
1/fck
p1 = 0.5
T = 4/6
p1 = 0.5
T = 1/6
Observe that the formula, T = 2 p1 (1 – p1), is not correct.

15. Cause for Error and Correction
Probability of transition is not independent of the present state of the signal.
Determine probability p 01 of a 0→1 transition.
Recognize p 01 ≠ p 0 × p 1
We obtain p 1 = (1 – p 1)p 01 + p 1 p 11
p 01
p 1 = ─────────
1 – p 11 + p 01

16. Correction (Cont.)
Since p 11 + p 10 = 1, i.e., given that the signal was previously 1, its present value can be either 1 or 0.
Therefore,
p 01
p 1 = ──────
p 10 + p 01
This uniquely gives signal probability as a function of transition probabilities.

17. Transition and Signal Probabilities
p01 = p10 = 0.5
p1 = 0.5
1/fck
p01 = p10 = 1/3
p1 = 0.5
p01 = p10 = 1/6
p1 = 0.5

18. Probabilities: p0, p1, p00, p01, p10, p11

19. Transition Density T = 2 p 1 (1 – p 1) = p 0 p 01 + p 1 p 10

20. Power Calculation
Power can be estimated if transition density is known for all signals.
Calculation of transition density requires
Signal probabilities
Transition densities for primary inputs; computed from vector statistics

21. Signal Probabilities x1 x2
x1 x2 x1 x2
x1 + x2 – x1x2 x1
1 - x1

22. Signal Probabilities 0.5 x1 x2 x3 x1 x2 0.5 0.25 0.625 0.5
y = 1 - (1 - x1x2) x3
= 1 - x3 + x1x2x3
= 0.625
X1 X2 X3 Y
Ref: K. P. Parker and E. J. McCluskey,
“Probabilistic Treatment of General Combinational Networks,” IEEE Trans. on Computers, vol. C-24, no. 6, pp , June 1975.

23. Correlated Signal Probabilities
0.5 x1 x2
x1 x2
0.5
0.25
0.625?
y = 1 - (1 - x1x2) x2
= 1 – x2 + x1x2x2
= 1 – x2 + x1x2
= 0.75 (correct value)
X1 X2 Y
0 0 1
0 1 0
1 0 1
1 1 1

24. Correlated Signal Probabilities
0.5
x1 + x2 – x1x2 x1 x2
0.5
0.75
0.375?
y = (x1 + x2 – x1x2) x2
= x1x2 + x2x2 – x1x2x2
= x1x2 + x2 – x1x2
= x2
= 0.5 (correct value)
X1 X2 Y
0 0 0
0 1 1
1 0 0
1 1 1

25. Observation
Numerical computation of signal probabilities is accurate for fanout-free circuits.

26. Remedies
Use Shannon’s expansion theorem to compute signal probabilities.
Use Boolean difference formula to compute transition densities.

27. Shannon’s Expansion Theorem
C. E. Shannon, “A Symbolic Analysis of Relay and Switching Circuits,” Trans. AIEE, vol. 57, pp , 1938.
Consider:
Boolean variables, X1, X2, , Xn
Boolean function, F(X1, X2, , Xn)
Then F = Xi F(Xi=1) + Xi’ F(Xi=0)
Where
Xi’ is complement of X1
Cofactors, F(Xi=j) = F(X1, X2, . . , Xi=j, . . , Xn), j = 0 or 1

28. Expansion About Two Inputs
F = XiXj F(Xi=1, Xj=1) + XiXj’ F(Xi=1, Xj=0)
+ Xi’Xj F(Xi=0, Xj=1)
+ Xi’Xj’ F(Xi=0, Xj=0)
In general, a Boolean function can be expanded about any number of input variables.
Expansion about k variables will have 2k terms.

29. Correlated Signal ProbabilitiesX1 X2
X1 X2
Y = X1 X2 + X2’
X1 X2 Y
0 0 1
0 1 0
1 0 1
1 1 1
Shannon expansion about the reconverging input, X2:
Y = X2 Y(X2 = 1) + X2’ Y(X2 = 0)
= X2 (X1) + X2’ (1)

30. Correlated Signals
When the output function is expanded about all reconverging input variables,
All cofactors correspond to fanout-free circuits.
Signal probabilities for cofactor outputs can be calculated without error.
A weighted sum of cofactor probabilities gives the correct probability of the output.
For two reconverging inputs:
f = xixj f(Xi=1, Xj=1) + xi(1-xj) f(Xi=1, Xj=0)
+ (1-xi)xj f(Xi=0, Xj=1) + (1-xi)(1-xj) f(Xi=0, Xj=0)

31. Correlated Signal ProbabilitiesX1 X2
X1 X2
Y = X1 X2 + X2’
Shannon expansion about the reconverging input, X2:
Y = X2 Y(X2=1) + X2’ Y(X2=0)
= X2 (X1) + X2’ (1)
y = x2 (0.5) + (1-x2) (1)
= 0.5 (0.5) + (1-0.5) (1)
= 0.75
X1 X2 Y
0 0 1
0 1 0
1 0 1
1 1 1

32. Example 0.5 Supergate 0.25 Point of reconv. 0.5 0.0 0.5 1.0 0.5 1 0.0
0.0
1.0
0.5
0.375
0.5
Reconv.
signal
Signal probability for supergate output
= 0.5 Prob{rec. signal = 1} Prob{rec. signal = 0}
= 0.5 × × 0.5 = 0.75
S. C. Seth and V. D. Agrawal, “A New Model for Computation of
Probabilistic Testability in Combinational Circuits,” Integration, the VLSI
Journal, vol. 7, no. 1, pp , April 1989.

33. Probability Calculation Algorithm
Partition circuit into supergates.
Definition: A supergate is a circuit partition with a single output such that all fanouts that reconverge at the output are contained within the supergate.
Identify reconverging and non-reconverging inputs of each supergate.
Compute signal probabilities from PI to PO:
For a supergate whose input probabilities are known
Enumerate reconverging input states
For each input state do gate by gate probability computation
Sum up corresponding signal probabilities, weighted by state probabilities

34. Calculating Transition Density1
Boolean
function
x1, T1 .
xn, Tn
y, T(Y) = ? n

35. Boolean Difference ∂Y Boolean diff(Y, Xi) = ── = Y(Xi=1) ⊕ Y(Xi=0) ∂Xi
Boolean diff(Y, Xi) = 1 means that a path is sensitized from input Xi to output Y.
Prob(Boolean diff(Y, Xi) = 1) is the probability of transmitting a toggle from Xi to Y.
Probability of Boolean difference is determined from the probabilities of cofactors of Y with respect to Xi.
F. F. Sellers, M. Y. Hsiao and L. W. Bearnson, “Analyzing Errors with
the Boolean Difference,” IEEE Trans. on Computers, vol. C-17, no. 7,
pp , July 1968.

36. Transition Density n T(y) = Σ T(Xi) Prob(Boolean diff(Y, Xi) = 1) i=1
F. Najm, “Transition Density: A New Measure of Activity in Digital
Circuits,” IEEE Trans. CAD, vol. 12, pp , Feb

37. Power Computation
For each primary input, determine signal probability and transition density for given vectors.
For each internal node and primary output Y, find the transition density T(Y), using supergate partitioning and the Boolean difference formula.
Compute power,
P = Σ 0.5CY V2 T(Y)
all Y
where CY is the capacitance of node Y and V is supply voltage.

38. Transition Density and Power
0.2, 1 X1 X2 X3
0.06, 0.7
0.3, 2 Ci
0.436, 3.24 Y
0.4, 3 CY
Transition density
Signal probability
Power = 0.5 V 2 (0.7Ci CY)

39. Prob. Method vs. Logic Sim.
Circuit
No. of gates
Probability method
Logic Simulation
Error %
Av. density
CPU s*
C432
160
3.46
0.52
3.39 63
+2.1
C499
202
11.36
0.58
8.57
241
+29.8
C880
383
2.78
1.06
3.25
132
-14.5
C1355
346
4.19
1.39
6.18
408
-32.2
C1908
880
2.97
2.00
5.01
464
-40.7
C2670
1193
3.50
3.45
4.00
619
-12.5
C3540
1669
4.47
3.77
4.49
1082
-0.4
C5315
2307
3.52
6.41
4.79
1616
-26.5
C6288
2406
25.10
5.67
34.17
31057
C7552
3512
3.83
9.85
5.08
2713
-24.2
* CONVEX c240

40. Problem 1
For equiprobable inputs analyze the 0→1 transition probabilities of all gates in the two implementations of a four-input AND gate shown below. Assuming that the gates have zero delays, which implementation will consume less average dynamic power? E E A B C D A B C D F G G F
Chain structure
Tree structure

41. Total transitions/vector
Problem 1 Solution
Given the primary input probabilities, P(A) = P(B) = P(C) = P(D) = 0.5, signal and transition (0→1) probabilities are as follows:
Signal
name
Chain
Tree
Prob(sig.= 1)
Prob(0→1)
Prob(sig.=1) E
0.2500
0.1875 F
0.1250
0.1094 G
0.0625
0.0586
Total transitions/vector
0.3555
0.4336
The tree implementation consumes 100×( – )/ = 22% more average dynamic power. This advantage of the chain structure may be somewhat reduced because of glitches caused by unbalanced path delays.

42. Problem 2
Assume that the two-input AND gates in Problem 1 each has one unit of delay. Find input vector pairs for each implementation that will consume the peak dynamic power. Which implementation consumes less peak dynamic power? E E A B C D A B C D F G G F
Chain structure
Tree structure

43. Problem 2 Solution
For the chain structure, a vector pair {A B C D} = {1110},{1011} will produce four gate transitions as shown below. A B C D E F G
A=11
B=10
E=10
C=11
F=10
D=01
G=00
Time units

44. Problem 2 Solution (Cont.)
The tree structure has balanced delay paths. So it cannot make more than 3 gate transitions. A vector pair {ABCD} = {1111},{1010} will produce three transitions as shown below. A B C D E F G
A=11
B=10
E=10
C=11
D=10
F=10
G=10
Time units
Therefore, just counting the gate transitions, we find that the chain consumes 100(4 – 3)/3 = 33% higher peak power than the tree.