  In many machine learning applications, e.g., tree-based ensembles, floating
point numbers are extensively utilized due to their expressiveness. Nowadays
performing data analysis on embedded devices from dynamic data masses becomes
available, but such systems often lack hardware capabilities to process
floating point numbers, introducing large overheads for their processing. Even
if such hardware is present in general computing systems, using integer
operations instead of floating point operations promises to reduce operation
overheads and improve the performance.
  In this paper, we provide \mdname, a full precision floating point comparison
for random forests, by only using integer and logic operations. To ensure the
same functionality preserves, we formally prove the correctness of this
comparison. Since random forests only require comparison of floating point
numbers during inference, we implement \mdname~in low level realizations and
therefore eliminate the need for floating point hardware entirely, by keeping
the model accuracy unchanged. The usage of \mdname~basically boils down to a
one-by-one replacement of conditions: For instance, a comparison statement in
C: if(pX[3]<=(float)10.074347) becomes
if((*(((int*)(pX))+3))<=((int)(0x41213087))). Experimental evaluation on X86
and ARMv8 desktop and server class systems shows that the execution time can be
reduced by up to $\approx 30\%$ with our novel approach.
