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**Timing Analysis - Delay Analysis Models**

Simple model 1: Ak Dk A3 A1 A2 Ak = arrival time = max(A1,A2,A3) + Dk Dk is the delay at node k, parameterized according to function fk and fanout node k Simple model 2: Ak Ak = max{A1+Dk1, A2+Dk2,A3+Dk3} Ak Dk3 Dk1 Dk2 A3 A1 A3 A2 A1 A2 Can also have different times for rise time and fall time

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**Static delay analysis Levelize Nodes: Compute arrival times:**

/* level of PI nodes initialized to 0, the others are set to -1. Invoke LEVEL from PO */ LEVEL(k) if( k.level != -1) return(k.level) else k.level = 1+max{LEVEL(ki)|ki fanin(k)} Compute arrival times: /* Given arrival times on PI’s */ for L = 0 to MAXLEVEL for {k|k.level = L} Ak = max{Aki} + Dk

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**Required times Required times: given required times on primary outputs**

Traverse in reverse topological order (i.e. from primary outputs to primary inputs) if (ki , k ) is an edge between ki and k, Rki ,k = Rk - Dk (this is the edge required time) Hence, the required time of output of node k is Rk = min ( Rk,kj | kj fanout(k) )

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**Propagating Slacks Slacks: slack at the output node k is Sk = Rk-Ak**

Since Rki,k=Rk-Dk Ski,k =Rki,k - Aki Ski,k + Aki = Rk-Dk = Sk + Ak - Dk Since Ak = max {Akj } + Dk Ski,k = Sk + max {Akj } - Aki kj , ki fanin (k ) Ski = min{Ski,j} j fanout (ki ) Notes: each edge is the graph has a slack and a required time Negative slack is bad. Sk Sj k j Ski ki ki

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**Sequential networks Arrival times known at l1 and l2**

C2 C1 l1 l4 C3 C4 l2 l3 l5 Arrival times known at l1 and l2 Required times known at l3, l4, and l5 Delay analysis gives arrival and required times (hence slacks) for C1, C2, C3, C4

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**Sequential Networks Note: Latch l5 may be transparent clock**

t1 is the beginning of latch transparent time Arrival time at l5 output = max { arrival time at l5 input, t1 } + Dlatch(hold) Required time at l5 input = min { required time at l5 output, t2 } - Dlatch(set_up) clock t1 t2

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**Static critical paths Min-Max problem: minimize max{-Si , 0}**

A static critical path of a Boolean network is a path P = {i1,i2,…,ip } where Sik, ik+1 < 0 Note: if a node k is on a static critical path, then at least one of the fanin edges of k is critical. Hence, all critical paths reach from an input to an output. Note: There may be several critical paths

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**Example: Static critical paths**

A1=6 R1=5 A2=5 R2=5 S1=-1 R3=3 S2=0 R7=1 S3,1=-1 R9=-1 S4,1 = -1 S4,2 = 0 S5,2 = 1 S6,3 = 0 S7,3 = -1 S7,4 = -1 S7,5 = 1 S8,6 = 0 S9,7 = -1 critical path edges R1=5 R2=5 6 -1 5 1 2 1 2 -1 1 4 -1 4 3 3 2 2 4 1 5 -1 -1 1 2 1 1 6 2 7 -1 9 8 A9=0 A8=0 Ski,k = Sk + max{Akj } - Aki , kj,ki fanin(k) Sk = Sk + min{Ski,k }, kj fanout(k)

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**Timing analysis problems**

We want to determine the true critical paths of a circuit in order to: determine the minimum cycle time that the circuit will function identify critical paths from performance optimization - don’t want to try to optimize the wrong (non-critical) paths Implications: Don’t want false paths (produced by static delay analysis) Delay model is worst case model. Need to ensure correctness for case where ith gate delay DiM

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**Functional Timing Analysis - How to Detect False Paths**

What is Timing Analysis? Estimate when the output of a given circuit gets stable Combinational block T clock The output needs to be stable by t=T for the correct functionality

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**Why Timing Analysis? Timing verification Timing optimization**

Verifies whether a design meets a given timing constraint Example: cycle-time constraint Timing optimization Needs to identify critical portion of a design for further optimization Critical path identification In both applications, the more accurate, the better

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**Timing Analysis - Basics**

Naïve approach - Simulate all input vectors with SPICE Accurate, but too expensive Gate-level timing analysis Focus of this lecture Less accurate than SPICE due to the level of abstraction, but much more efficient Scenario: Gate/wire delays are pre-characterized (accuracy loss) Perform timing analysis of a gate-level circuit assuming the gate/wire delays

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**Gate-level Timing Analysis**

A naive approach is topological analysis Easy longest-path problem Linear in the size of a network Not all paths can propagate signal events False paths If all longest paths are false, topological analysis gives delay overestimate Functional timing analysis = false-path-aware timing analysis Compute false-path-aware arrival time False path aware arr(z)? z 1 x1 x2 arr(x1)=0 arr(x2)=0

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**Example: 2-bit Carry-skip Adder**

c_in s0 Length 1 Length 5 a0 s1 b0 1 a1 c_out b1 mux ripple carry adder

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End of lecture 19

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**False Path Analysis - Basics**

Is a path responsible for delay? If the answer is no, can ignore the path for delay computation Check the falsity of long paths until we find the longest true path How can we determine whether a path is false? Delay underestimation is unacceptable Can lead to overlooking a timing violation Delay overestimation is not desirable, but acceptable Topological analysis can give overestimate, but never give underestimate

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**Controlling/Non-Controlling Values**

Controlled value of AND 1 Controlling value of AND Non-Controlling value of AND 1 Controlling value of OR Controlled value of OR Non-Controlling value of OR

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Static Sensitization A path is statically-sensitizable if there exists an input vector such that all the side inputs to the path are set to non-controlling values This is independent of gate delays Controlling value! 1 t=0 These paths are not statically-sensitizable 1 The longest true path is of length 2?

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**Static Sensitization The (dashed) path is responsible for delay!**

Delay underestimation by static sensitization (delay = 2 when true delay = 3) incorrect condition 1 2 3 1 2 1

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**What is Wrong with Static Sensitization?**

The idea of forcing non-controlling values to side inputs is okay, but timing was ignored The same signal can have a controlling value at one time and a non-controlling value at another time. How about timing simulation as a correct method?

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**Timing Simulation 2 1 4 Implies that delay = 0 for these inputs BUT! 2**

2 3 1 4 Implies that delay = 0 for these inputs BUT!

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**Timing Simulation 2 1 1 1 4->2**

2 1 3 2 1 1 3 4 1 4->2 2 Implies that delay = 4 with the same set of inputs.

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**What is Wrong with Timing Simulation?**

If gate delays are reduced, delay estimates can increase Not acceptable since Gate delays are just upper-bounds Delay uncertainty due to manufacturing We are implicitly analyzing a family of circuits where gate delays are within the upper-bounds

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**Monotone Speedup Property**

Definition: For any circuit C, if C’ is obtained from C by reducing some gate delays, and delay_estimate(C’) delay_estimate(C), then delay_estimate has Monotone Speedup property Timing simulation does not have this property

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**Timing Simulation Revisited**

2 2 1 4 3 4 1 4 means that the rising signal occurs anywhere between t = -infinity and t = 4. 4 X-valued simulation

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**Timing Simulation Revisited**

Timed 3-valued (0,1,X) simulation called X-valued simulation Monotone speedup property is satisfied. Underlying model of floating mode condition [Chen, Du] Applies to “simple gate” networks only viability [McGeer, Brayton] Applies to general Boolean networks

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**Floating Mode Analysis**

Assume that all nodes have X before applying an input vector conservative assumption Path sensitization (floating-mode - for simple gate networks only) For final controlled output value 1 Earliest-arriving controlling value determines the output stable time late early 1 For final non-controlled output value Latest-arriving non-controlling value determines the output stable time early late

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**False Path Analysis Algorithms**

Checking the falsity of every path explicitly is too expensive - exponential # of paths State-of-the-art approach: Start: set L = Ltop - ! = topological longest path delay - ! Lold = 0 Binary search: If (Delay(L)) (*) !L = (L-Lold)/2, Lold = L, L = L + !L Else, !L = (L-Lold)/2, Lold = L, L = L - !L If (L > Ltop or !L < threshold), L = Lold , done (*) Delay(L) = 1 if there an input vector under which an output gets stable only at time t where L t ? Can be reduced to a SAT problem [McGeer, Saldanha, Brayton, ASV] or a timed-ATPG [Devadas, Keutzer, Malik]

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**SAT-based False Path Analysis**

Decision problem: Is there an input vector under which the output gets stable only after t = T ? Idea: characterize the set of all input vectors S(T) that make the output stable no later than t = T check if S(T) contains S = all possible input vectors This check is solved as a SAT problem: Is S \ S(T) empty? - set difference + emptiness check Let F and F(T) be the characteristic functions of S and S(T) Is F !F(T) satisfiable?

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Example d g a b e f c Assume all the PIs arrive at t = 0, all gate delays = 1 Is the output stable time t > 2?

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**Example g(1,t=2) : the set of input vectors under which**

g gets stable to value = 1 no later than t =2 a b c d e f g Onset: stabilized by t=2? g(1,t=2) = d(1,t=1) Ç f(1,t=1) = (a(0,t=0) Ç b(0,t=0)) Ç (c(1,t=0) È e(1,t=0)) = !a!b(c È Æ) = !a!bc = S1(t=2) g(1,t=¥) = onset = !a!bc = g(1,t=2) = S1

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**Example g(0,t=2) : the set of input vectors under which**

g gets stable to value = 0 no later than t=2 a b c d e f g g(0,t=2) = d(0,t=1) È f(0,t=1) = (a(1,t=0) È b(1,t=0)) È (c(0,t=0) Ç e(0,t=0)) = (a+b) + (!c Ç Æ) = a+b = S0(t=2) g(0,t=¥) = offset = a+b+!c = S0

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**Example g(0,t=2) : the set of input vectors under which**

g gets stable to 0 no later than t=2 a b c d e f g Offset: NOTstabilized by t=2 under abc=000 g(0,t=2) = a+b g(0,t=¥) = offset = a+b+!c g(0,t=¥) \ g(0,t=2) = (a+b+!c) !(a+b) = !a !b !c = satisfiable

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For general networks Can build a companion network to represent each output: output(0,t=T’) or output(1,t=T’) Take each gate g from outputs in reverse topological order. For g(1,t=T): Compute all primes of gate onset Make AND gate for each prime OR these together to form g(1,t=T) For g(0,t=T), do same but for offset For inputs to these primes, form companion networks for {fi(1,t = T-Dg), fi(0,t = T-Dg), i in FI(g)} fi(1,T-Dg) p1 Now form SAT clauses for this network and solve g(1,T) p2 + fj(0,T-Dg) p3

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**Summary False-path-aware arrival time analysis is well-understood**

Practical algorithms exist Can handle industrial circuits easily Remaining problems Incremental analysis (make it so that a small change in the circuit does not make the analysis start all over) Integration with logic optimization DSM issues such as cross-talk-aware false path analysis

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**Timed ATPG Yet another way to solve the same decision problem**

A generalization of regular ATPG: regular ATPG find an input vector that differentiates a fault-free circuit and a faulty circuit in terms of functionality Timed ATPG find an input vector that exhibits a given timed behavior Timed extension of PODEM

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Timed ATPG (0,[3,3]) (X,[0,0])

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**Timed ATPG (X,[1,1]) (0,[3,3]) (0,[0,0]) (X,[2,3]) (0,[1,1]) (X,[0,0])**

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**Timed ATPG (1,[1,1]) (0,[3,3]) (0,[0,0]) (X,[2,3]) (0,[1,1]) (X,[1,2])**

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**Timed ATPG (1,[1,1]) (0,[3,3]) (0,[0,0]) (1,[2,2]) (0,[1,1]) (1,[1,1])**

(1,[0,0]) (1,[2,2]) (0,[1,1]) (1,[1,1]) Value and time conflict! Backtrack!

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**Timed ATPG (1,[1,1]) (0,[3,3]) (0,[0,0]) (0,[3,3]) (0,[1,1])**

Justified! (0,[2,2]) Found an input vector that makes the output stable to 0 no earlier than t=3

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