The rapid advancement of generative AI and large language models has long positioned “compute power” as the core of the industry, with market attention heavily centered on GPUs. However, as AI applications evolve from model training to large-scale inference, extend from cloud to edge devices, and further progress toward AI agents and multi-model collaboration architectures, system bottlenecks are gradually shifting. The AI industry is moving away from a GPU-centric paradigm toward a new stage of CPU + GPU + memory co-optimized computing.
In inference and AI agent scenarios, system complexity increases significantly. CPUs are responsible for task scheduling, logical control, and data preprocessing, GPUs handle core model inference computation, while memory becomes the critical hub for data and model state movement. AI agents continuously invoke tools, maintain short-term and long-term memory, and perform multi-step reasoning, making the overall system behave more like an integrated system engineering problem rather than pure acceleration.
Within this architecture, data movement cost is rapidly rising and is becoming a key performance bottleneck. Model weights and intermediate features must frequently travel between CPUs, GPUs, and various accelerators, while inference outputs must also be synchronized and integrated in real time. As model scale and agent task complexity increase simultaneously, memory bandwidth, latency, and cache efficiency become decisive factors for system throughput and user experience.
As a result, memory is shifting from a supporting role to a core component. Whether it is HBM, LPDDR, or on-chip SRAM and cache, all form the foundational layer of AI system performance. In particular, within AI SoCs, SRAM not only serves as high-speed storage but also directly impacts latency, power efficiency, and overall computational performance, making memory design as critical as compute units in system-level competition.
However, as the proportion of memory in chips continues to grow, reliability challenges are also amplified. Process variation, aging effects, and workload stress can all lead to SRAM faults or latent error accumulation. This elevates “testability” and “repairability” into essential capabilities for both mass production and long-term system operation—an area that ISTART-TEK has long been focused on.
ISTART-TEK provides comprehensive solutions for on-chip memory in AI processors. Through MBIST and MBISR architectures, combined with User-Defined Algorithms (UDA), it improves fault diagnosis efficiency and coverage, enabling memory issues to be accurately identified and repaired at an early stage, thereby reducing downstream risks and field failure rates.
In addition, ISTART-TEK’s MART (MBIST Algorithm Recommendation Tool) further enhances intelligence in memory testing and repair workflows. It can automatically recommend optimal test and repair strategies based on different memory types, defect characteristics, and application scenarios, improving testing efficiency and shortening development cycles. In the context of highly customized and rapidly iterating AI chips, such tool-based capabilities are becoming a critical foundation for improving production efficiency.
Overall, AI infrastructure competition is no longer a contest of GPU compute power alone, but is shifting toward system-level efficiency across CPU, GPU, and memory. As computing architectures become more integrated and heterogeneous, the stability and repairability of memory will directly determine the scalability and reliability of AI systems. At this inflection point in the industry, memory test and repair technologies have become an essential foundation supporting the continued expansion of the AI era.