How accurately can soft error impact be estimated in black-box/white-box cases? - a case study with an edge AI SoC -

Artificial intelligence (AI) edge devices often feature numerous storage units and sequential logic circuits, making them vulnerable to soft errors. For reliable and critical edge AI applications, assessing System-on-Chip (SoC) reliability in advance is essential. Here, there are two cases: a self-designed SoC (white-box), or a commercial off-the-shelf (COTS) chip (black-box). This study uses alpha particle irradiation results on our 22nm AI SoC as a golden reference to estimate soft error impacts, injecting faults across the entire chip in the white-box case and into the accessible memory and registers in the black-box case. The results demonstrate a high degree of consistency between the white-box case and golden reference, meaning that pre-silicon reliability assessment is feasible. As for the black-box case, the proportion of memory in the SoC remains unchanged and is still significantly larger than that of registers, and hence the simulation results between black-box and white-box are not substantially different.