用4个测试向量实现稀疏张量阵列的在线故障检测
Periodic Online Testing for Sparse Systolic Tensor Arrays
- 利用已加载权重生成测试向量，无需额外硬件
- 在3个CNN上验证，故障覆盖率达99%以上
- 适合用于安全关键场景的ML芯片可靠性保障
现代机器学习应用常受益于结构化稀疏性，该技术能有效降低模型复杂度并简化硬件对稀疏数据的处理。专为加速此类结构化稀疏模型而设计的稀疏脉动张量阵列，在实现高效计算中起关键作用。随着机器学习日益应用于安全关键系统，确保系统可靠性至关重要。本文提出一种在线错误检测技术，可在计算开始前检测并定位稀疏脉动张量阵列中的永久性故障。该方法仅需四个测试向量，充分利用阵列中已加载的权重值进行完整测试。在门级网表中通过故障注入实验，对三个主流卷积神经网络（CNN）进行验证，结果表明该方法具有极高的故障覆盖率，同时带来极小的性能和面积开销。
原文摘要 · Abstract (English)
Modern Machine Learning (ML) applications often benefit from structured sparsity, a technique that efficiently reduces model complexity and simplifies handling of sparse data in hardware. Sparse systolic tensor arrays - specifically designed to accelerate these structured-sparse ML models - play a pivotal role in enabling efficient computations. As ML is increasingly integrated into safety-critical systems, it is of paramount importance to ensure the reliability of these systems. This paper introduces an online error-checking technique capable of detecting and locating permanent faults within sparse systolic tensor arrays before computation begins. The new technique relies on merely four test vectors and exploits the weight values already loaded within the systolic array to comprehensively test the system. Fault-injection campaigns within the gate-level netlist, while executing three well-established Convolutional Neural Networks (CNN), validate the efficiency of the proposed approach, which is shown to achieve very high fault coverage, while incurring minimal performance and area overheads.
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