* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download ASPDAC2010_Wanping - Computer Science and Engineering
Sound level meter wikipedia , lookup
Signal-flow graph wikipedia , lookup
Power inverter wikipedia , lookup
Electrical ballast wikipedia , lookup
Immunity-aware programming wikipedia , lookup
Variable-frequency drive wikipedia , lookup
Power engineering wikipedia , lookup
Three-phase electric power wikipedia , lookup
Electrical substation wikipedia , lookup
Current source wikipedia , lookup
Schmitt trigger wikipedia , lookup
History of electric power transmission wikipedia , lookup
Resistive opto-isolator wikipedia , lookup
Opto-isolator wikipedia , lookup
Power electronics wikipedia , lookup
Voltage regulator wikipedia , lookup
Power MOSFET wikipedia , lookup
Surge protector wikipedia , lookup
Buck converter wikipedia , lookup
Switched-mode power supply wikipedia , lookup
Rectiverter wikipedia , lookup
Stray voltage wikipedia , lookup
Alternating current wikipedia , lookup
Voltage optimisation wikipedia , lookup
On-Chip Power Network Optimization with Decoupling Capacitors and Controlled-ESRs Wanping Zhang1,2, Ling Zhang2, Amirali Shayan2, Wenjian Yu3, Xiang Hu2, Zhi Zhu1, Ege Engin4, Chung-Kuan Cheng2 1Qualcomm Inc. 5775 Morehouse Dr., San Diego, U.S.A 2UC San Diego, U.S.A 3Tsinghua University, Beijing 100084, China 4San Diego State University, U.S.A Outline of Optimization with Decap and Controlled-ESR Introduction Existing works add decap to reduce noise Controlled-ESR is shown to be effective to suppress the resonance Power network model with controlled-ESR Problem statement Power network noise considering overshoot Formulation Revised sensitivity computation Sensitivity computation with merged adjoint network Revised sensitivity computation considering voltage overshoot SQP based optimization Experimental results Conclusions 2 Our Contributions We propose to allocate decaps and controlled-ESRs simultaneously to suppress the resonance and reduce SSN of power network. We consider both voltage drop and overshoot for voltage violation. Derive revised sensitivity. An optimization formulation with the objective function of minimizing the voltage violation area and a constraint of decap budget is presented, and solved with an efficient SQP algorithm. 3 Power Network Model with Controlled-ESR VDD Current Source Inductor VDD ControlledESR Decap Resistor 4 Voltage Variation Analysis with Circuit State Equation If add extra decap ⊿C and controlled-ESR ⊿A, solution x will be updated by ⊿x, so (2) becomes: The solution of (4) is: (1) (2) Cx Ax Bu which is denoted to be: By subtracting (2) from (3): 0 v G E v T BU L i E R i C 0 From KCL and KVL, we have the circuit state equation: (C C )( x x) ( A A)( x x) Bu (3) (C C )x ( A A)x (Ax Cx) (4) x e 1 C A( t t0 ) t x0 e C 1 A( t ) C 1U ( )d (5) t0 where: C C C, A A A, U Ax Cx 2 3 A A eA I A ... 5 2! 3! Effect of Controlled-ESR on reducing the noise C ontrolled-E S R = 10 m O hm C ontrolled-E S R = 100 m O hm 1.1 C ontrolled-E S R = 1 O hm C ontrolled-E S R = 10 O hm VDD ControlledESR Decap of V[V0] (V) Voltage V oltage V0 1.05 1 0.95 0.9 0.85 0.8 0 2 4 6 8 10 12 14 16 18 20 Tim e [ns ] Time (ns) 6 Power Network Noise Considering Overshoot V V V io la tio n A re a Vm ax Vdd Vdd Vmin V m in Violation Area ts T V io la tio n A re a te g j max(Vmin v j (t ), 0)dt 0 T t s1 te 1 ts 2 te2 T T g j max[max(Vmin v j (t ), 0), max(v j (t ) Vmax , 0)]dt 0 7 Problem Formulation Objective function: N Min g j 1 j Constraints: (1) Voltage response satisfies the circuit equation with given stimulus; M (2) Total decap budget: ci Q i 1 (3) Space constraint for each decap location: 0 ci cmax i (4) Space constraint for each controlled-ESR location: 0 CtrlESRi CtrlESRmax i 8 Sensitivity Computation with Merged Adjoint Network The sensitivity sij is defined to be the contribution of decap added at node i to remove violation at node j: sij g j ci The merged adjoint sensitivity is defined to be the contribution of decap added at node i to remove the violation for all nodes. The merged adjoint network has a current source applied at every node j u (t ts ) u (t te ) Merged adjoint sensitivity is calculated with N T j 1 0 si sij (vi ,all (T t )) vi (t )dt , (i 1, 2,..., M ) 9 Revised Sensitivity Computation Considering Overshoot T We denote the port currents and voltages by vectors Ip and Vp. Denote the nonsource branch currents and voltages by vectors Ib and Vb. From Tellegen’s theorem, we have [iˆ ( )v p p (t ) vˆ p ( )i p (t )] T t dt 0 T [iˆb ( )vb (t ) vˆb ( ) ib (t )] T t dt 0 We set all voltage sources in the adjoint network to zero and apply a current source for each violation node: I s Dk [u(t tsk ) u (t tek )] k 1 1, if v(tsk ) Vdd Dk 1, if v(tsk ) Vdd Nv g Dk [u (t tsk ) u (t tek )]v p (t ) dt T t 0 k 1 T Left hand: g [vˆc ( )vc (t )] T t dt C 0 T Right hand: Nv sC g sR [iˆR ( )iR (t )] T 10t dt R 0 T SQP Based Optimization Algorithm for the SQP based optimization: 1. Select the intrinsic capacitance and controlled-ESR to be the initial solution X(0). 2. Simulate the power network circuit, and compute the sensitivity as gradient using the revised method. 3. Use the gradient to approximate the problem with a linearly constrained QP subproblem at X(t). 4. Solve for the step size d(t) to move. 5. If meet with termination condition, stop; Else, let X(t+1) = X(t) + d(t). 6. Increase t and return to step 2. 11 A Simple Case P1 CtrlESR 2 CtrlESR 3 Decap 1 Decap 2 Decap 3 + - R = 1 ohm and L=1 nH. Decap=0.01 nF, controlled-ESR = 1.0e-4 ohm. Vdd is 1V, and the allowable voltage drop is 0.05V. Maximum allowable decap at each node to be 0.1 nF, Total decap should not exceed 0.2 nF, Maximum allowable controlled-ESR at each node is 0.2 Ohm Without optimization: The overall noise is 193.7 V*ps. Optimize with decap only: CtrlESR 1 Constraints: P3 Initial values: P2 Decap at each node are: 0.1 nF, 0.09 nF, and 0.01 nF. The noise after optimization is 6.3 V*ps. Optimize with both decap and controlled-ESR: Controlled-ESR values at each node are: 0.2 Ohm, 2.77e-2 Ohm, and 1.0e-4 Ohm. The noise is further reduced to be 5.3 V*ps. The controlled-ESR improves the noise by 15.9%. 12 Experimental Results Table I. Effect of considering voltage overshoot The noise is the voltage violation area and the number of violation nodes. Total noise is on average underestimated by 4.8% due to neglecting the voltage overshoot. The number of violation nodes is almost the same for both cases. 13 Experimental Results Table II. Comparison among three methods for the minimization of power network noise The noise (column 2, 4, 6) and the number of violation nodes (column 3, 5, 7) are reduced. The improvement brought by considering the controlled-ESRs is 25% on average. With the third method, the average allocated controlled-ESR ranges from 0.038 Ohm to 0.083 Ohm for different cases. Voltage Waveforms with Different Optimizations 1.06 Without optimization Optimization with evenly distributed decap SQP result with decap only SQP result with decap and Controlled-ESR (V) Voltage Voltage [V] 1.04 1.02 1 0.98 0.96 0.94 0 5 10 Time [ns] 15 20 Time (ns) 15 Relationship between Decap Budget and Noise 350 (V*ps) Noise Noise [V*ps] 300 250 200 150 100 50 0 5 10 15 20 25 30 Decap [nF] 35 40 45 50 Decap (nF) Larger decap budget leads to smaller noise Tradeoff between the noise reduction and the decap investment 16 Conclusions Optimize power network with both decap and controlled-ESR. Revised sensitivity computation considering voltage overshoot. SQP based optimization 17 Thank You! Q&A 18