Customized application-specific processors called ASIPs are becoming commonplace in contemporary embedded system designs. Neural networks are an interesting application for which an ASIP can be tailored to increase performance, lower power consumption and/or increase throughput. Here, both the bidirectional associative memory and hopfield auto-associative memory networks are run through an automated instruction-set identification algorithm to identify and select custom instruction candidates suitable for neural network applications. Clusters of neural networks are highly parallel, and therefore it is interesting to consider a homogeneous multiprocessor composed of ASIPs. The two legacy neural network applications showed a 18-120% improvement with the automatic hardware/software partitioning for a uniprocessor ASIP. However, due to pointers and function calling which did not resolve to hardware, the acceleration was concentrated in the network initialization part of the code.