Conventional DDNs are "growing wider and deeper, with the complexity growing from millions to hundreds of millions of parameters in the last few years," a DARPA presolicitation document says. "The basic computational primitive to execute training and inference functions in DNN is the multiply and accumulate (MAC) operation. As DNN parameter count increases, SOA networks require tens of billions of MAC operations to carry out one inference." This means that the accuracy of DNN "is fundamentally limited by available MAC resources," DARPA says. "Consequently, SOA high accuracy DNNs are hosted in the cloud centers with clusters of energy hungry processors to speed up processing. This compute paradigm will not satisfy many DoD applications which demand extremely low latency, high accuracy artificial intelligence (AI) under severe size, weight, and power constraints."