Which scaling rule applies to large artificial neural networks Technological limitations for biology-imitating computing

Experience shows that cooperating and communicating computing systems, comprising segregated single processors, have severe performance limitations, which cannot be explained using von Neumann's classic computing paradigm. In his classic First Draft, he warned that using a too fast processor vitiates his simple procedure (but not his computing model!); furthermore, that using the classic computing paradigm for imitating neuronal operations is unsound. Amdahl added that large machines, comprising many processors, have an inherent disadvantage. Given that artificial neural network's (ANN's) components are heavily communicating with each other, they are built from a large number of components designed/fabricated for use in conventional computing, furthermore they attempt to mimic biological operation using improper technological solutions, and their achievable payload computing performance is conceptually modest. The type of workload that artificial intelligence-based systems generate leads to an exceptionally low payload computational performance, and their design/technology limits their size to just above the toy level systems: The scaling of processor-based ANN systems is strongly nonlinear. Given the proliferation and growing size of ANN systems, we suggest ideas to estimate in advance the efficiency of the device or application. The wealth of ANN implementations and the proprietary technical data do not enable more. Through analyzing published measurements, we provide evidence that the role of data transfer time drastically influences both ANNs performance and feasibility. It is discussed how some major theoretical limiting factors, ANN's layer structure and their methods of technical implementation of communication affect their efficiency. The paper starts from von Neumann's original model, without neglecting the transfer time apart from processing time, and derives an appropriate interpretation and handling for Amdahl's law. It shows that, in that interpretation, Amdahl's law correctly describes ANNs.

Automated summary

The limitations of cooperating and communicating computing systems, the impact of processor speed on computing performance, the inefficiency of large machines, and the challenges in mimicking biological operations using artificial neural networks are discussed. The low computational performance of AI systems and the influence of communication methods on efficiency are highlighted. The study also introduces ideas to estimate the efficiency of devices or applications in advance, considering the nonlinear scaling of processor-based ANN systems.