Quantitative comparison of architectures for digital neuro-computers

As the field of neural networks matures toward real-world applications, a strong need for hardware systems to efficiently compute larger networks arises. Comparisons between neural network-dedicated platforms are often fragmentary or qualitative. An architectural-level evaluation scheme is attempted by proposing some performance indices on idealized architecture classes. Some conclusions on the advantages and limitations of the architectures are drawn.