VLSI/CAD Laboratory Department of Computer Science National Tsing Hua University TH EDA Estimation of Maximum Instantaneous Current for Sequential Circuits.

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Presentation transcript:

VLSI/CAD Laboratory Department of Computer Science National Tsing Hua University TH EDA Estimation of Maximum Instantaneous Current for Sequential Circuits Cheng-Tao Hsieh and Shih-Chieh Chang National Tsing Hua University Hsinchu, Taiwan

2 Maximum Instantaneous Current (MIC) To calculate the MIC, must decide which input vectors and at which time. 0 0 MIC=3 at time t=3 MIC=4 at time t=1. t=1t=2t=3

3 Two Types of Methods Vector dependent  Deriving the worst case vectors  Lower bound estimation Vectorless  No vector search  Upper bound estimation

4 Two Types of Methods Vector dependent  Deriving the worst case vectors  Lower bound estimation Vectorless  No vector search  Upper bound estimation

5 Vectorless Methods Definition: Two gates are Mutually Exclusive Switching (MES) at time t 1 if they cannot switch simultaneously at t 1.  [C.T. Hsieh, J.C. Lin, and S.C. Chang, accepted by TCAD] The two transitions cannot occur simultaneously

6 Combinational Correlation Signal correlation in a combinational circuit. The two transitions cannot occur simultaneously

7 Sequential Correlation Correlation across sequential elements. (f 1, f 2 )= (0, 0) (0, 1) (1, 0) (1, 1) f2f2 f1f1 t=0 t=1

8 Impact from Sequential Correlations Accuracy loss if ignore sequential correlations.

9 The Use of Real Delay Model Do not impact on accuracy but impact on efficiency.  The number of transitions on a gate may be exponential to the circuit size. [H. Kriplani, et al., TCAD’95]  Large memory and run time to detect MES among many transitions.

10 Solution for Efficiency Problem Detect MES in a time interval instead of at an exact time instant. time t1t1 t2t2 t3t3 Time interval t 1 to t 3

11 Trade-off Between Accuracy and Efficiency Larger time interval  more efficient but less accurate. Circuit C7552

Accuracy of MIC Estimation circuitDelayiMax Our approach C C C C C C C C C C Avg

13 Two Types of Methods Vector dependent  Deriving the worst case vectors  Lower bound estimation Vectorless  No vector search  Upper bound estimation

14 Vector Dependent Methods GA-based, probability-based, ILP-based, and modified timed ATPG algorithm.  [Y.M. Jiang, A. Krstic, and K.-T. Cheng, TVLSI, ’00]. Modified timed ATPG algorithm can derive better results than other methods. Timed ATPG is not scalable.

15 A Timed ATPG Problem A transition:  A logic change v  v ’ at a certain time t 1. Find a vector pair satisfying both functional and temporal conditions. Temporal condition Functional condition

16 An Example of Timed ATPG (a 1,b 1,c 1 ), (a 2,b 2,c 2 ) = (0,0,1), (1,1,0) a b c g g=0  1 at t=2 a b c g 1 1 t=2

17 Transition Characteristic Function Definition: A transition characteristic function (TCF),  g=01, t=t1 (v 1, v 2 ), characterizes all vector pairs v 1 and v 2 which causes gate g to have a rising transition at time t=t 1.

18 An Example of TCF (a 1,b 1,c 1 ), (a 2,b 2,c 2 ) = (0,0,1), (1,1,0) (0,0,1), (1,1,1) (1,0,1), (1,1,0) (1,0,1), (1,1,1) (0,1,0), (1,1,0) (0,1,0), (1,1,1) (0,1,1), (1,1,0) (0,1,1), (1,1,1)  g=01, t=2 = a 1 ’b 1 ’c 1 a 2 b 2 c 2 ’ + a 1 ’b 1 ’c 1 a 2 b 2 c 2 + a 1 b 1 ’c 1 a 2 b 2 c 2 ’ + a 1 b 1 ’c 1 a 2 b 2 c 2 + a 1 ’b 1 c 1 ’a 2 b 2 c 2 ’ + a 1 ’b 1 c 1 ’a 2 b 2 c 2 + a 1 ’b 1 c 1 a 2 b 2 c 2 ’ + a 1 ’b 1 c 1 a 2 b 2 c 2 + a b c g g=0  1 at t=2

19 Construction of TCF Construct a TCF by extracting information from circuit structure. A TCF is represented in the multi-level form, more compact than the two-level form.

20 An Example  g=01, t=2 = (a 1 b 1 +b 1 ’c 1 ’)’(a 2 b 2 +b 1 ’c 1 ’) a b c g a1a1 b1b1 c1c1 a2a2 b2b2  g=01, t=2 g=0  1 at t=2

21 Sequential Correlation a b c Flip-flop b1b1 c1c1 b2b2  g=01, t=2 a1a1 b1b1 c1c1 a2a2 The second vector on input b depends on the first vector.

Initial Experimental Results circuit#PIs#gates RandomOursMES MIC time (s)MIC s s s s s s s s s s s Avg

23 Conclusion Propose vectorless and vector dependent estimation for the MIC. Consider sequential correlations, which can significantly impact the MIC estimation.

24 Acknowledge Prof. Shih-Chieh Chang Jian-Cheng Lin Yu-Min Kuo Yue-Lung Chang Download: