Power Estimation

Dr. Elwin Chandra Monie

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Abstraction, Complexity, Accuracy

Abstraction levelComputing resources Analysis accuracy

Algorithm Least Worst

Software and system

Hardware behavior

Register transfer

Logic

Circuit

Device Most Best

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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.

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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.

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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

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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.

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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.

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Gate-Level Power Analysis

• Sum up all three components of power:

P = Pcap + Pint + Pstat

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Switching Frequency

Number of transitions per unit time:

N(t)T = ───

t

For a continuous signal:

N(t)T = lim ───

t→∞ t

T is defined as transition density.

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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

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Static Transition Probabilities

• 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)

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Static Transition Frequency

0 0.25 0.5 0.75 1.0

0.25

0.2

0.1

0.0

p1

f =

p1

(1 –

p1

)

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Inaccuracy in Transition Density

1/fck

p1 = 0.5 T = 1.0

p1 = 0.5 T = 4/6

p1 = 0.5 T = 1/6

Observe that the formula, T = 2 p1 (1 – p1), is not correct.

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Inaccuracy in Transition Density

1/fck

p1 = 0.5 T = 1.0

p1 = 0.5 T = 4/6

p1 = 0.5 T = 1/6

Observe that the formula, T = 2 p1 (1 – p1), is not correct.

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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 01p 1 = ─────────

1 – p 11 + p 01

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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 01p 1 = ──────

p 10 + p 01This uniquely gives signal probability as a function of transition probabilities.

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Transition and Signal Probabilities

1/fck

p01 = p10 = 0.5 p1 = 0.5

p01 = p10 = 1/3 p1 = 0.5

p1 = 0.5p01 = p10 = 1/6

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

• p 01 + p 00 =1• p 11 + p 10 = 1• p 0 = 1 – p 1

p 01

p 1 = ───────

p 10 + p 01

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Transition Density

• T = 2 p 1 (1 – p 1) = p 0 p 01 + p 1 p 10

= 2 p 10 p 01 / (p 10 + p 01)

= 2 p 1 p 10 = 2 p 0 p 01

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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

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Signal Probabilities

x1

x2

x1 x2

x1

x2

x1 + x2 – x1x2

x1 1 - x1

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Signal Probabilities x1

x2 x3

x1 x2

y = 1 - (1 - x1x2) x3 = 1 - x3 + x1x2x3 = 0.625X1 X2 X3 Y

0 0 0 10 0 1 00 1 0 10 1 1 01 0 0 11 0 1 01 1 0 11 1 1 1

0.5

0.5

0.5

0.25 0.625

Ref: K. P. Parker and E. J. McCluskey,“Probabilistic Treatment of General Combinational Networks,” IEEE Trans. on Computers, vol. C-24, no. 6, pp. 668-670, June 1975.

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Correlated Signal Probabilities

x1

x2

x1 x2

y = 1 - (1 - x1x2) x2 = 1 – x2 + x1x2x2 = 1 – x2 + x1x2 = 0.75 (correct value)

X1 X2 Y0 0 10 1 01 0 11 1 1

0.5

0.5 0.25 0.625?

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Correlated Signal Probabilities

x1

x2

x1 + x2 – x1x2

y = (x1 + x2 – x1x2) x2 = x1x2 + x2x2 – x1x2x2 = x1x2 + x2 – x1x2 = x2 = 0.5 (correct value)

X1 X2 Y0 0 00 1 11 0 01 1 1

0.5

0.5 0.75 0.375?

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Observation

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

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Remedies

• Use Shannon’s expansion theorem to compute signal probabilities.

• Use Boolean difference formula to compute transition densities.

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Shannon’s Expansion Theorem

• C. E. Shannon, “A Symbolic Analysis of Relay and Switching Circuits,” Trans. AIEE, vol. 57, pp. 713-723, 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

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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.

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Correlated Signal Probabilities

X1

X2

X1 X2

X1 X2 Y0 0 10 1 01 0 11 1 1

Y = X1 X2 + X2’

Shannon expansion about the reconverging input, X2:

Y = X2 Y(X2 = 1) + X2’ Y(X2 = 0) = X2 (X1) + X2’ (1)

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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)

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Correlated Signal Probabilities X1

X2

X1 X2

X1 X2 Y0 0 10 1 01 0 11 1 1

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

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Example

Point of reconv.

Supergate0.5

0.5

0.5

0.5

0.25

10

0.50.0

0.01.0

0.51.0

Signal probability for supergate output = 0.5 Prob{rec. signal = 1} + 1.0 Prob{rec. signal = 0} = 0.5 × 0.5 + 1.0 × 0.5 = 0.75

0.375

Reconv. signal

S. C. Seth and V. D. Agrawal, “A New Model for Computation ofProbabilistic Testability in Combinational Circuits,” Integration, the VLSI Journal, vol. 7, no. 1, pp. 49-75, April 1989.

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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

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Calculating Transition Density

Boolean function

1

n

x1, T1..... xn, Tn

y, T(Y) = ?

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Boolean Difference

• 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.

∂YBoolean diff(Y, Xi) = ── = Y(Xi=1) ⊕ Y(Xi=0)

∂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. 676-683, July 1968.

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Transition Density

nT(y) = Σ T(Xi) Prob(Boolean diff(Y, Xi) = 1)

i=1

F. Najm, “Transition Density: A New Measure of Activity in DigitalCircuits,” IEEE Trans. CAD, vol. 12, pp. 310-323, Feb. 1993.

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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.

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Transition Density and Power

X1

X2 X3

0.2, 1

0.3, 2

0.4, 3

0.06, 0.7

0.436, 3.24

Transition densitySignal probability

YCi

CY

Power = 0.5 V 2 (0.7Ci + 3.24CY)

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Prob. Method vs. Logic Sim.

CircuitNo. of gates

Probability method Logic Simulation

Error%Av.

densityCPU s* Av.

densityCPU 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 -26.5

C7552 3512 3.83 9.85 5.08 2713 -24.2* CONVEX c240

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Problem 1For 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?

Chain structure Tree structure

ABCD

EF

G

ABCD

E

F

G

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Problem 1 SolutionGiven the primary input probabilities, P(A) = P(B) = P(C) = P(D) = 0.5, signal and transition (0→1) probabilities are as follows:

Signalname

Chain Tree

Prob(sig.= 1)

Prob(0→1)

Prob(sig.=1)

Prob(0→1)

E 0.2500 0.1875 0.2500 0.1875

F 0.1250 0.1094 0.2500 0.1875

G 0.0625 0.0586 0.0625 0.0586

Total transitions/vector

0.3555 0.4336

The tree implementation consumes 100×(0.4336 – 0.3555)/0.3555 = 22% more average dynamic power. This advantage of the chain structure may be somewhat reduced because of glitches caused by unbalanced path delays.

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Problem 2Assume 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?

Chain structure Tree structure

ABCD

EF

G

ABCD

E

F

G

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Problem 2 SolutionFor the chain structure, a vector pair {A B C D} = {1110},{1011} will produce four gate transitions as shown below.

A

BCD

EF

G

A=11

B=10

E=10

C=11

F=10

D=01

G=00

Time units0 1 2 3

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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=10Time units

0 1 2 3

Therefore, just counting the gate transitions, we find that the chain consumes 100(4 – 3)/3 = 33% higher peak power than the tree.