Efficient Functional In-Field Self-Test for Deep Learning Accelerators
Efficient Functional In-Field Self-Test for Deep Learning Accelerators
复制标题
深度学习加速器的高效功能现场自测试
DOI：
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发表时间：
2021
期刊：
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通讯作者：
Yanjing Li
中科院分区：
文献类型：
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作者：
Yi He;T. Uezono;Yanjing Li
我们提出了一种专门针对深度学习 (DL) 加速器生成高质量功能现场自测试的技术。这些功能测试可以在深度学习加速器正常运行期间应用于现场，这对于确保满足任何给定应用的安全和/或可靠性要求至关重要，包括自动驾驶汽车、机器人等安全关键应用。我们的技术利用特殊的架构特征和应用属性来实现高功能测试覆盖率，同时将系统级成本降至最低。此外，我们为计算单元（支持计算操作）和控制单元（控制数据移动）设计了不同的策略，因为这两种类型的单元表现出不同的属性。对于DL加速器的计算单元，我们首先使用组合ATPG来生成具有高测试覆盖率的测试模式，这是可能的，因为这些单元不包含复杂的顺序逻辑。接下来，我们将 ATPG 模式映射到一个或多个可直接在加速器上执行的等效深度神经网络 (DNN)，考虑到 DL 加速器的明确定义的数据流/重用算法，这是可能的。对于控制单元，我们利用了在许多应用领域（例如自动驾驶汽车）中通常一次仅部署一个或几个固定 DNN 的特性。因此，仅针对可能直接影响当前部署的 DNN 正确性的故障就足够了。这是通过使用精心设计的输入和权重值执行每个目标 DNN 的不同层来实现的，以最大限度地提高测试覆盖率，同时最大限度地缩短测试时间。我们使用 Nvidia 的开源加速器作为案例研究来应用我们的技术，以展示其功效。我们的结果表明我们的技术实现了高测试覆盖率。对于计算单元，实现了 99.9% 的单一固定功能测试覆盖率。对于控制单元，我们能够证明，给定任何目标 DNN，对于一大类单故障和多故障模型都可以实现 100% 的覆盖率。现场功能自检时间也非常短，对于各种代表性的 DNN，< 17 ms。这些功能测试可以在启动、重置期间甚至与正常操作同时进行，通过直接在加速器上执行 DNN 测试程序，无需硬件中的任何测试支持。
We present a technique that generates high-quality functional in-field self-tests specifically targeting deep learning (DL) accelerators. These functional tests can be applied in the field during normal operation of a DL accelerator, which is crucial to ensure that the safety and/or reliability requirements are met for any given application, including safety-critical applications such as self-driving cars, robotics, and more.Our technique takes advantage of special architectural characteristics and application properties to achieve high functional test coverage while incurring minimal system-level costs. Moreover, we devise different strategies for the compute units (which support computation operations) and the control units (which control data movement) because these two types of units exhibit different properties. For the compute units of a DL accelerator, we first use combinational ATPG to generate test patterns with high test coverage, which is possible because these units do not contain complex sequential logic. Next, we map the ATPG patterns to one or more equivalent deep neural networks (DNNs) that can be directly executed on the accelerator, which is possible given the well-defined dataflow/reuse algorithm of a DL accelerator. For the control units, we leverage the property that typically only one or a few fixed DNNs are deployed at a time in many application domains (e.g., self-driving cars). Thus, it is sufficient to target only the faults that can directly affect the correctness of the DNNs that are currently deployed. This is done by executing different layers of each target DNN using carefully-crafted input and weight values to maximize test coverage while minimizing test time.We apply our technique using Nvidia’s open-source accelerator as a case study to demonstrate its efficacy. Our results show that our technique achieves high test coverage. For the compute units, 99.9% single stuck-at functional test coverage is achieved. For the control units, we are able to prove that, given any target DNN, 100% coverage can be achieved for a large class of single and multiple fault models. The in-field functional self-test time is also very low, < 17 ms for various representative DNNs. These functional tests can be applied during boot-up, reset, and even concurrently with normal operation by executing DNN test programs directly on the accelerator, without requiring any test support in the hardware.