Throughout the evolution of computing technology, exponential growth in processor performance, rapid increases in memory capacity, and continuous breakthroughs in network bandwidth have consistently overshadowed a critical reality: the I/O bottleneck of storage systems has remained the "Achilles' heel" constraining overall performance. The root of this problem traces back to the foundation of modern computer architecture—the von Neumann architecture.

The von Neumann architecture, proposed in 1945, established the fundamental design paradigm for modern computers: consisting of five major components (arithmetic logic unit, control unit, memory, input, and output devices) and operating on the "stored-program" principle where both instructions and data reside in memory. While this revolutionary design laid the groundwork for modern computing, it simultaneously planted the seeds for performance constraints. The architecture's core characteristics—sequential execution and shared memory—led to the well-known "von Neumann bottleneck": at any given moment, the CPU can only exchange data with memory through bandwidth-limited channels.

The storage bottleneck in traditional computing architectures stems from significant disparities in the development rates of various technical components. Analyzing the fundamental structure of computer storage hierarchy provides clearer insight into this issue:

The tabular data reveals astonishing performance gaps between different storage tiers, with latency differences spanning multiple orders of magnitude. This developmental imbalance creates a severe reality: powerful computing cores frequently remain idle while waiting for data supply, analogous to a supercar constrained to rugged mountain roads, unable to unleash its true performance potential.

In AI application scenarios, this problem becomes dramatically amplified. Modern neural network training requires processing terabyte-scale datasets, while models with hundreds of billions of parameters present unprecedented demands on data supply. The sequential execution characteristics of the von Neumann architecture create fundamental conflicts with the highly parallelized nature of AI workloads. Research indicates that in typical AI training environments, computational losses due to storage bottlenecks can reach 30-50%. This not only represents massive resource waste but significantly impedes the pace of AI technological innovation.

Conventional optimization approaches prove inadequate in addressing this challenge. Whether exploring flash potential through NVMe protocols, optimizing network latency via RDMA technology, or improving data locality through intelligent caching algorithms, these point solutions fail to resolve the fundamental issue. Much like widening individual roads in an outdated urban plan, they cannot solve systemic traffic congestion.

The deeper issue lies in the von Neumann architecture's design philosophy, which struggles to adapt to AI era requirements. Traditional architectures assume sequential, localized data access patterns, while AI workloads exhibit highly concurrent, random access, massive-scale characteristics. This fundamental paradigm mismatch determines that any optimization based on traditional architecture cannot achieve breakthrough progress.

We now stand at an inflection point for storage architecture revolution. Piecemeal improvements can no longer meet the computational demands of the AI era, necessitating a paradigm-level transformation in storage architecture. This requires thinking beyond the conventional framework of von Neumann architecture and reimagining the future landscape of storage architecture from a systems perspective.

The new generation of storage architecture must achieve breakthroughs across multiple dimensions while preserving the essence of von Neumann's "stored-program" concept: breaking the physical boundaries between storage and computation to establish a compute-near-memory paradigm; eliminating performance barriers between storage tiers to enable intelligent data movement; and creating adaptive data pathways that can perceive AI workload characteristics. This fundamental transformation at the architectural level will become crucial for unleashing computational potential.

The paradigm revolution in storage architecture represents not merely an evolution of the von Neumann architecture, but a core driving force for computational breakthroughs in the AI era. When we successfully construct new storage architectures truly adapted to AI workloads, we will not only overcome current computational bottlenecks but also establish a solid foundation for the evolution of next-generation computing paradigms. This revolution will enable us to fully unleash computational potential and seize opportunities in the data deluge of the intelligent era.