In this research, we propose modified FR-Vector model, named FR-Vector with BAM which combined the advantage of FR-Vector － glitch processing in non-zero delay model, and the advantage of Boolean approximation method － the ability of processing correlation among convergent circuit. This model could analyze the switching activities in combinational circuit in probabilistic way with few data.
We have solved the correlation due to temporal dependence (using switching probability) and spatial correlation due to internal spatial dependence (using BAM to solve reconvergent circuit). Though it still has input spatial correlations, we have successively improved the error percentage in each gate from 5.05 to 2.27 and from 6.09 to 2.78 which represents input pattern with glitches and without glitches
respectively. The peak value of error percentage also decreases from 23.74 to 13.24 %.
In time issue, for we use the concept of Taylor expansion to approximate the spatial correlation, we do not need to build OBDD of the whole circuit which adopted in most techniques. Thus, the time spend by FR-Vector with BAM is closed to the original FR-Vector, and compare to the simulation time of NC-VHDL, it could up to 33.73 times faster. Indeed, the larger the circuit is or the more input patterns there is, we will get the more speed-up for the time is depend on the size of circuits.
As mentioned before, there is still something which could be improved in
FR-Vector with BAM, that is, the spatial dependency among inputs. Thought in larger circuit (may be long depth or a big number of gates), the input correlations could only do little affect. But in special case, such as small circuits or circuits with specific input patterns, it still would be a serious problem. For circuits without specific input
patterns, we could simulate them in various input patterns to eliminate the error
caused by input spatial correlations, but for circuits with specific input patterns, we could only find out some way to process the dependency for controlling the error.
Another point could be improved is the way represent signal transition or the way we propagate the BAM data structure. In probabilistic calculation, the most correct consequence would appear when the probabilities in each state are balance.
However, in FR-Vector, the probability of transition F and R would be by far smaller than the probability of transition 1 and 0 in no doubt, and result in an inaccuracy. So we should find a new way to represent the transition probabilities, or we should find a method that could calculate diverge values of probabilities without losing accuracy.
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