Assessing the reliability of FPGA-Based Quantised Neural Networks under Neutron Irradiation

SRAM Field-Programmable Gate Arrays (FPGAs) are popular computing platforms for implementing neural networks (NNs) due to their flexibility and low recurring engineering costs. Nevertheless, reliability concerns arise due to their susceptibility to radiation effects, especially considering high-altitude or -scalability applications. In this work, we explore the resilience of quantised, and especially binarised (e.g., use binary values to represent the weights) and nearly binarised, FPGA NNs to neutron-induced errors. Specifically, we study the impact of various NN design parameters, such as the degree of quantisation and parallelisation, the type of memory for storing the weights, and the diversity of the input images on the NN reliability. To achieve this, we exposed to accelerated atmospheric-like neutron radiation a large set of NN designs built through FINN, a state-of-the-art development framework targeting FPGA NN accelerators, using different quantisation, folding and weight storage schemes. We examine how these parameters affect the tradeoff between area, performance and reliability, calculating various metrics such as the dynamic cross-section, Failures In Time (FIT), and Mean Executions Between Failures (MEBF). Our findings show that less quantisation (i.e., larger bit precision), as well as less folding (i.e., more parallelisation), leads to improved reliability (i.e., lower FIT and higher MEBF). In contrast, these two factors push the performance and area of the NNs in opposite directions (i.e. less quantisation and folding result in lower latency but more FPGA resources). Moreover, the results reveal that the choice of the memory type (i.e. Block RAM or distributed RAM) for storing the NN weights impacts the reliability metrics, with the mixed memory configuration providing the highest reliability.