EA-based Resynthesis: An Efficient Tool for Optimization of Digital Circuits

Cartesian genetic programming, Evolutionary resynthesis, Logic optimization

Since the early nineties the lack of scalability of fitness evaluation has been
the main bottleneck preventing the adoption of evolutionary algorithms for logic
circuits synthesis. Recently, various formal approaches such as SAT and BDD
solvers have been introduced to this field to overcome this issue. This made it
possible to optimise complex circuits consisting of hundreds of inputs and
thousands of gates. Unfortunately, we are facing another problem-scalability of
representation. The efficiency of the evolutionary optimization applied at the
global level deteriorates with the increasing complexity. To overcome this issue,
we propose to apply the concept of local resynthesis in this work. Local
resynthesis is an iterative process based on the extraction of smaller
sub-circuits from a complex circuit that are optimized locally and implanted back
to the original circuit. When applied appropriately, this approach can mitigate
the problem of scalability of representation. Two complementary approaches to the
extraction of the sub-circuits are presented and evaluated in this work. The
evaluation is done on a set of highly optimized complex benchmark problems
representing various real-world controllers, logic and arithmetic circuits. The
experimental results show that the evolutionary resynthesis provides better
results compared to globally operating evolutionary optimization. In more than
85% cases, a substantially higher number of redundant gates was removed while
keeping the computational effort at the same level. A huge improvement was
achieved especially for the arithmetic circuits. On average, the proposed method
was able to remove 25.1% more gates.

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