摘要：The Competition for Authenticated Encryption: Security, Applicability, and Robustness(CAESAR)supported by the National Institute of Standards and Technology(NIST) is an ongoing project calling for submissions of authenticated encryption(AE) schemes. The competition itself aims at enhancing both the design of AE schemes and related analysis. The design goal is to pursue new AE schemes that are more secure than advanced encryption standard with Galois/counter mode(AES-GCM) and can simultaneously achieve three design aspects: security,applicability, and robustness. The competition has a total of three rounds and the last round is approaching the end in 2018. In this survey paper, we first introduce the requirements of the proposed design and the progress of candidate screening in the CAESAR competition. Second, the candidate AE schemes in the final round are classified according to their design structures and encryption modes. Third, comprehensive performance and security evaluations are conducted on these candidates. Finally, the research trends of design and analysis of AE for the future are discussed.

摘要：Spiking neural network,inspired by the human brain,consisting of spiking neurons and plastic synapses,is a promising solution for highly efficient data processing in neuromorphic ***,memristor-based neurons and synapses are becoming intriguing candidates to build spiking neural networks in hardware,owing to the close resemblance between their device dynamics and the biological ***,the functionalities of memristor-based neurons are currently very limited,and a hardware demonstration of fully memristor-based spiking neural networks supporting in-situ learning is very ***,a hybrid spiking neuron combining a memristor with simple digital circuits is designed and implemented in hardware to enhance neuron *** hybrid neuron with memristive dynamics not only realizes the basic leaky integrate-and-fire neuron function but also enables the in-situ tuning of the connected synaptic ***,a fully hardware spiking neural network with the hybrid neurons and memristive synapses is experimentally demonstrated for the first time,and in-situ Hebbian learning is achieved with this *** work opens up a way towards the implementation of spiking neurons,supporting in-situ learning for future neuromorphic computing systems.