Many function approximation procedures can obtain enhanced accur acyby an efficient table lookup of a product z=f(x)g(y). Both x and y are represented by indices of i leading bits (typically 7 < i <16) for arguments normalized to [0,1] or [1,2]. Direct bipartite lookup employs \frac {i}{2} bits each of x and y yielding roughly an \frac {i}{2} bit result which can lose 2 to 3 bits of accuracy when f and g are nonlinear. Indirect bipartite lookup first generates \frac {i}{2} bit interval index values for f(x) and g(y) using separate j-bits-in \frac {i}{2} -bits-out tables for f(x) and g(y) where \frac {i}{2} < j < i and is chosen large enough to substantially reduce the effect of non linearity in f(x) and g(y). The separate tables readily compensate for the high non linearity in f and/or g and generate interval index values representing intervals that can be tailored to minimize the maximum error of the product z=f(x)g(y) determined by an interval product table with the concatenated interval indices as the i bit input. We describe several variations in interval index generation methodology and in the design of the interval product table lookup architecture so as to obtain accuracy of i bits (or better) in output in 2-3 cycles of table lookup latency.