As the AI industry shifts from large-scale model training toward inference and Edge AI deployment, the priorities in chip design are also changing. In the past, competition among AI chips was largely centered on compute scale and parallel processing capability. Today, however, what often determines system efficiency is whether data inside the chip can be moved and accessed quickly, reliably, and with low power consumption. This has significantly increased the importance of the core data-flow subsystems in AI inference architectures—SRAM, cache, and buffer.
Among them, SRAM is primarily responsible for high-speed data storage and real-time computational support. During AI inference, large volumes of model weights, intermediate computation results, and activation data must be repeatedly read and written. If the system relies entirely on external DRAM, latency increases significantly and power consumption rises accordingly. As a result, many AI accelerators and Edge AI processors integrate large amounts of SRAM as on-chip local memory to maintain high throughput and low-latency performance.
However, as SRAM capacity and quantity continue to increase, the risk of memory defects also rises. Common issues in advanced process nodes—such as weak bits, unstable data retention, read disturbance, and resistive defects—may gradually become more severe under high-frequency inference or prolonged operation, potentially impacting AI model stability and overall DPPM performance.
In addition to SRAM, cache architecture is rapidly becoming a core component of AI chip design. One of the biggest differences between AI workloads and traditional CPU workloads is the extremely high level of data reuse. The same set of model weights and feature data may be repeatedly accessed by numerous compute units within a short period of time. If cache efficiency is insufficient, the system must frequently fetch data from external memory, which not only slows inference speed but also increases power consumption and memory bandwidth pressure.
To address this, many AI SoCs are adopting multi-level cache, shared cache, and distributed cache architectures to improve data reuse efficiency. However, as cache structures become increasingly complex, issues related to data coherence, timing variation, and access stability also become more prominent. Especially under high-temperature, low-voltage, or long-duration AI operating conditions, cache reliability is gradually becoming one of the key factors affecting AI system stability.
At the same time, buffer is receiving increasing attention. AI inference involves intensive data-flow scheduling, requiring continuous data exchange and synchronization among different compute modules. As a result, buffer architecture directly affects overall data-flow efficiency. From input buffers and weight buffers to feature buffers, buffers serve not only as temporary storage but also as mechanisms for balancing data-flow rates between different compute nodes.
If buffer design or quality is unstable, issues such as data congestion, timing mismatch, or data loss may occur, ultimately reducing overall throughput. This is particularly critical in Edge AI and real-time inference applications, where buffer latency and stability directly impact system responsiveness and real-time performance.
As SRAM, cache, and buffer capacities continue to scale in AI chips, memory testing and repair strategies must also evolve. Traditional fixed memory testing flows are no longer sufficient to comprehensively address the diverse memory architectures and workload behaviors found in modern AI systems.
To meet these challenges, iSTART-TEK’s MART, UDA, and TEC technologies provide a more flexible and customizable SRAM testing framework.
MART (MBIST Algorithm Recommendation Tool) is an AI-driven algorithm analysis system that helps users quickly identify suitable SRAM testing algorithms based on application type, DPPM targets, power consumption, performance requirements, and area constraints. It effectively simplifies algorithm selection and test planning.
UDA (User-Defined Algorithms) provides a modular, building-block-like platform that allows users to define fundamental testing elements and combine them into customized memory testing algorithms. Through this modular approach, engineers can develop testing flows tailored to the specific characteristics of different SRAM, cache, and buffer architectures, improving testing flexibility and defect coverage.
TEC (Testing Elements Change) enables test engineers to adjust or reassemble SRAM testing algorithms during CP and FT stages according to different testing environments. Since SoC testing often involves extreme voltage and temperature conditions, different environments may correspond to different types of memory defects. TEC helps engineers rapidly customize testing elements and build alternative algorithms optimized for specific requirements.
As the AI industry enters an era of large-scale inference and long-duration operation, SRAM, cache, and buffer are no longer secondary components. They have become fundamental elements that determine AI chip performance, power efficiency, yield, and reliability. Future competition in AI chips will increasingly depend on the quality and testability of the memory subsystem itself.