How Amdahl's Law limits the performance of large artificial neural networks : why the functionality of full-scale...

No prior work had fully resolved why large-scale neural network simulations encounter performance plateaus despite increasing hardware power. Researchers often struggle to replicate the massive connectivity of biological brains using standard digital architectures. That uncertainty drove investigations into the fundamental constraints of modern computing paradigms. Prior research has shown that neural networks possess inherent parallel structures suitable for high-speed processing. However, these models frequently run on electronic circuits that operate through sequential clock cycles. This gap motivated a closer look at how traditional hardware design conflicts with the massive parallelism required for artificial intelligence. The current landscape of supercomputing relies heavily on architectures developed decades ago. These legacy systems may not be optimized for the unique demands of complex, brain-scale neural simulations.

The aim of this study is to analyze why current supercomputers and simulators struggle to scale up artificial neural networks to the level of biological brains. The authors investigate the specific technical reasons behind the performance plateaus observed in large-scale artificial intelligence applications. This work seeks to clarify the role of legacy computing paradigms in restricting the potential of modern hardware. The researchers address the conflict between the inherently parallel nature of neural networks and the sequential operation of current electronic systems. The study explores the impact of clock-driven architectures on the efficiency of parallelized computing tasks. The authors intend to explain the saturation of performance that has hindered previous attempts at massive network simulation. This research provides a theoretical basis for understanding the limitations of current hardware and software approaches. The motivation for this study is to provide a clear explanation for the persistent challenges in achieving high-performance brain-scale simulations.

The review approach involves a critical examination of current supercomputing architectures relative to their capacity for simulating complex neural systems. The authors evaluate the compatibility between sequential-parallel computing paradigms and the inherent parallelism found in artificial intelligence models. This assessment utilizes historical data regarding the performance of various hardware and software simulators. The investigation focuses on the structural limitations imposed by clock-driven electronic circuits within these platforms. The researchers synthesize findings from previous studies to identify patterns of performance saturation. This analysis compares the theoretical expectations of parallel systems with the observed outcomes in large-scale simulations. The review approach prioritizes the identification of fundamental bottlenecks that persist across different types of computing environments. The authors systematically map the constraints of the seventy-year-old computing paradigm onto the requirements of modern neural network applications.

Key findings from the literature demonstrate that all current neural network simulators face a hard performance ceiling due to the deployment of sequential-parallel computing elements. The authors report that the saturation observed in former studies is a direct consequence of the mathematical constraints defined by Amdahl's Law. The data indicate that the reliance on clock-driven electronic circuits prevents these systems from scaling linearly with added hardware resources. The review reveals that the seventy-year-old computing paradigm is fundamentally ill-suited for the massive parallelism required by brain-scale simulations. The findings show that both software and hardware-based simulators suffer from these identical structural limitations. The authors highlight that the exponentially increasing demand for computing capacity cannot be met by simply adding more sequential processors. The literature confirms that the inherent parallel operation of neural networks is consistently undermined by the sequential nature of current electronic architectures. The synthesis shows that performance gains diminish rapidly as the scale of the simulated network increases.

The authors propose that Amdahl's Law serves as a primary constraint for all current neural network simulation platforms. Synthesis and implications suggest that sequential-parallel architectures cannot overcome these mathematical barriers to achieve true brain-scale performance. The researchers argue that the reliance on clock-driven electronic circuits inherently limits the scalability of these systems. This review indicates that saturation in performance observed in previous studies stems from these foundational architectural constraints. The authors conclude that the seventy-year-old computing paradigm remains incompatible with the requirements of massive, parallel neural networks. These findings imply that future progress requires moving beyond traditional sequential processing models. The analysis highlights that hardware and software simulators face identical bottlenecks when scaling up artificial intelligence applications. The synthesis demonstrates that the current trajectory of supercomputing development will continue to encounter diminishing returns for large-scale neural models.