Area Constraint Aware Physical Unclonable Function for Intelligence Module

Abstract

Artificial intelligence technology such as neural network (NN) is widely used in intelligence module for Internet of Things (IoT). On the other hand, the risk of illegal attacks for IoT devices is pointed out; therefore, security countermeasures such as an authentication are very important. In the field of hardware security, the physical unclonable functions (PUFs) have been attracted attention as authentication techniques to prevent the semiconductor counterfeits. However, implementation of the dedicated hardware for both of NN and PUF increases circuit area. Therefore, this study proposes a new area constraint aware PUF for intelligence module. The proposed PUF utilizes the propagation delay time from input layer to output layer of NN. To share component for operation, the proposed PUF reduces the circuit area. Experiments using a field programmable gate array evaluate circuit area and PUF performance. In the result of circuit area, the proposed PUF was smaller than the conventional PUFs was showed. Then, in the PUF performance evaluation, for steadiness, diffuseness, and uniqueness, favorable results were obtained.