Modular Functional Testing: Targeting the Small Embedded Memories in GPUs

 Modern graphics processing units (GPUs) are manufactured using cutting-edge technologies but are prone to suffer from in-field errors and reliability issues [1]. The flexibility and computational power of GPUs push their adoption in developing advanced driver-assistance systems (ADASs) and sensor fusion solutions in the automotive and autonomous systems domains. However, the premature aging and wear-out features in new transistor technologies promote the rising of permanent faults during the in-field operation. In safety-critical applications, unaffordable failures caused by faults can induce the entire system to fail or even result in catastrophic consequences if no appropriate measures are taken promptly. Hence, the development of countermeasures for the in-field detection of faults is of great importance in GPUs. Publishedworks, addressing in-field fault detection for GPUs, can be classified into three classes: 1) design for testability (DfT) methods, which are purely hardware-oriented; 2) hybrid approaches, which combine hardware structures with reconfigurable capabilities at the software level; and 3) software-based self-test (SBST) solutions. DfT schemes are widely used for the end-of-production test in current devices. However, they are not always available for in-field operation and may not satisfy time constraints in many applications. Furthermore, hybrid solutions, based on the addition or use of available structures (i.e., performance counters) to extend the fault observability of a module, must be included in the design phases by modifying the hardware-software interface to provide instruction-based control of the included structures. Jagannadha et al. [2] proposed an in-system-test architecture based on the combination of DfT schemes and hybrid structures to detect faults and provide diagnosis features during the in-field operation of system-on-chips (SoCs) and GPUs. However, a massive effort is required to

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“…The modular approach is effectively applied to complex units in a GPU, such as the DMU and the embedded memories [10].…”

Section: General Interestmentioning

confidence: 99%