Assessing the Reliability of FPGA-Based Quantized Neural Networks Under Neutron Irradiation

Abstract

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 quantized, and especially binarized (e.g., use binary values to represent the weights) and nearly binarized, FPGA NNs to neutron-induced errors. Specifically, we study the impact of various NN design parameters, such as the degree of quantization and parallelization, 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 quantization, 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 (DCS), failures in time (FIT), and mean executions between failures (MEBF). Our findings show that less quantization (i.e., larger bit precision), as well as less folding (i.e., more parallelization), 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 quantization 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 (BRAM) or distributed RAM) for storing the NN weights impacts the reliability metrics, with the mixed memory configuration providing the highest reliability.