Tensor Processing Unit Reliability Dependence on Temperature and Radiation Source

Several applications in which autonomous capabilities are required, such as unmanned probes used for space exploration, require high reliability but impose strict power budget limits. To enable autonomy, convolutional neural networks (CNNs) are heavily employed to detect objects in images or frames. Recently, to adapt the computing power to the application needs, the market made available several low-power and low-cost commercial-off-the-shelf (COTS) computing solutions, called EdgeAI accelerators, that are very attractive for executing neural networks with limited power requirements. Since autonomous vehicles may operate on a wide range of temperatures, it is fundamental to understand the dependence of EdgeAI device error rate on the temperature. In this work, we consider Google’s Coral Edge Tensor Processing Unit (TPU) and measure its atmospheric neutrons reliability at different temperatures, which goes from −40 °C to +90 °C. We show a decrease in the failures in time (FIT) rate of almost
$4\times $
as temperature increases. Moreover, we compare the thermal neutrons and heavy ion cross sections of the TPU at room temperature. We report a difference of up to
$\sim 31\times $
between atmospheric and thermal neutrons cross section on the TPU. The TPU’s cross section for heavy ions is
$\sim 20\times $
higher than for atmospheric neutrons and
$\sim 187\times $
than thermal neutrons.