Analyzing the Structural and Operational Impact of Faults in Floating-Point and Posit Arithmetic Cores for CNN Operations

This work reports a first attempt to evaluate the fine-grain impact of permanent faults in the structures of arithmetic hardware cores implementing two number formats (Posit and FP). We assess and analyze errors in the cores for two operations (Add, and Multiply), which are the most used in several modern applications, including machine learning. The results show that Posit cores are structurally more vulnerable to fault propagation and induce more output corruptions than FP cores (from 3.3% up to 6.2%). Moreover, we found that the average absolute error in faulty FP cores is higher by up to 2 orders of magnitude than in Posit ones.