Any good reference for digital architecture implementations of floating point arithmetic operations?
There are ANN techniques that don't require floating-point. You will find things much easier if you adopt one of those.

Jim Austin's work at York University comes to mind. He uses FPGAs and fixed-point DSPs.

EDIT: (Leon, I hope you don't mind me adding this list here)


"fpga neural networks" discussion forum, in particular a FPGA NN where "The neuron uses 8bit numbers".
"FPGA Implementations of Neural Networks - a Survey of a Decade of Progress" by Zhu and Sutton: lists many implementation techniques, and briefly mentions that floating point implementation technique has not yet been successful.
"FPGA neural net" search on Google Scholar