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Whatmough, P. N., Lee, S. K., Lee, H., Rama, S., Brooks, D., Wei, G. Y..  2017.  14.3 A 28nm SoC with a 1.2GHz 568nJ/prediction sparse deep-neural-network engine with \#x003E;0.1 timing error rate tolerance for IoT applications. 2017 IEEE International Solid-State Circuits Conference (ISSCC). :242–243.

This paper presents a 28nm SoC with a programmable FC-DNN accelerator design that demonstrates: (1) HW support to exploit data sparsity by eliding unnecessary computations (4× energy reduction); (2) improved algorithmic error tolerance using sign-magnitude number format for weights and datapath computation; (3) improved circuit-level timing violation tolerance in datapath logic via timeborrowing; (4) combined circuit and algorithmic resilience with Razor timing violation detection to reduce energy via VDD scaling or increase throughput via FCLK scaling; and (5) high classification accuracy (98.36% for MNIST test set) while tolerating aggregate timing violation rates \textbackslashtextgreater10-1. The accelerator achieves a minimum energy of 0.36μJ/pred at 667MHz, maximum throughput at 1.2GHz and 0.57μJ/pred, or a 10%-margined operating point at 1GHz and 0.58μJ/pred.

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Lee, H., Cho, S., Seong, J., Lee, S., Lee, W..  2020.  De-identification and Privacy Issues on Bigdata Transformation. 2020 IEEE International Conference on Big Data and Smart Computing (BigComp). :514—519.

As the number of data in various industries and government sectors is growing exponentially, the `7V' concept of big data aims to create a new value by indiscriminately collecting and analyzing information from various fields. At the same time as the ecosystem of the ICT industry arrives, big data utilization is treatened by the privacy attacks such as infringement due to the large amount of data. To manage and sustain the controllable privacy level, there need some recommended de-identification techniques. This paper exploits those de-identification processes and three types of commonly used privacy models. Furthermore, this paper presents use cases which can be adopted those kinds of technologies and future development directions.

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Bruce, N., Kim, H., Kang, Y., Lee, Y., Lee, H..  2015.  On Modeling Protocol-Based Clustering Tag in RFID Systems with Formal Security Analysis. 2015 IEEE 29th International Conference on Advanced Information Networking and Applications. :498–505.

This paper presents an efficiency and adaptive cryptographic protocol to ensure users' privacy and data integrity in RFID system. Radio Frequency Identification technology offers more intelligent systems and applications, but privacy and security issues have to be addressed before and after its adoption. The design of the proposed model is based on clustering configuration of the involved tags where they interchange the data with the reader whenever it sends a request. This scheme provides a strong mutual authentication framework that suits for real heterogeneous RFID applications such as in supply-chain management systems, healthcare monitoring and industrial environment. In addition, we contribute with a mathematical analysis to the delay analysis and optimization in a clustering topology tag-based. Finally, a formal security and proof analysis is demonstrated to prove the effectiveness of the proposed protocol and that achieves security and privacy.