abstract = "Approximate computing is capable of exploiting the
error resilience of various applications with the aim
of improving their parameters such as performance,
energy consumption and area on a chip. In this paper, a
new systematic approach for the approximation and
optimization of circuits intended for LUT-based field
programmable gate arrays (FPGAs) is proposed. In order
to deliver a good trade-off between the quality of
processing and implementation cost, the method employs
a genetic programming-based optimization engine. The
circuits are internally represented and optimized at
the gate level. The resulting LUT-based netlists are
obtained using a commercial FPGA tool. In the
experimental part, four commonly available commercial
FPGA design tools (Xilinx ISE, Xilinx Vivado,
Precision, and Quartus) and state-of-the-art academia
circuit synthesis and optimization tool ABC are
compared. The quality of approximated circuits is
evaluated using relaxed equivalence checking by means
of Binary decision diagrams. An important conclusion is
that the improvements (i.e. area reductions) at the
gate level are preserved by the FPGA design tools and
thus the number of LUTs is also adequately reduced. It
was shown that the current state-of-the-art synthesis
tools provide (for some instances) the results that are
far from an optimum. For example, a 40percent reduction
(68 LUTs) was achieved for clmb benchmark circuit (Bus
Interface) without introducing any error. Additional
43percent reduction can be obtained by introducing only
a 0.1percent error.",