Small scale and low latency oriented neural network physical unclonable function and its evaluation

In edge computing, edge AI that is oriented to low-latency implementation is attracting attention. Also, with the development of deep learning in recent years, the scale of neural networks implemented on edge AI has been increasing. Therefore, small scale implementation of edge AI is important. On the other hand, individual authentication of semiconductors is urgently needed due to increasing the threat of counterfeit semiconductors. For this reason, an NN PUF has been proposed that implements both Neural Network (NN) and Physical Unclonable Function (PUF) as the individual authentication function of semiconductors. The conventional NN PUF is difficult to reduce the circuit scale due to the large implementation overhead. Therefore, this study proposes a small scale and low latency oriented new NN PUF based on the conventional method. In addition, evaluation experiments using an evaluation board verify the performance for NN and PUF. © 2020 The Institute of Electrical Engineers of Japan.