Special Issue "Energy-Efficient and Reliable Information Processing: Computing and Storage"
Deadline for manuscript submissions: closed (31 March 2019).
Interests: Coding and information theory for nanoscale devices, resource-constrained machine learning, energy-efficient computing and storage
Recently, artificial intelligence (AI) systems have begun to approach and exceed human performance in many intelligent tasks: AlexNet  and ResNet  achieving human-level accuracy in recognition tasks; AlphaGo  beating human champions in Go. These acclaimed successes of AI are mainly based on computations using massive amounts of data. Two pillars of modern AI systems are computation and data, and this information processing has to take place efficiently and reliably. The main aim of this Special Issue is to seek high-quality submissions that address energy-efficient and reliable computing and data storage systems. The topics of interest include, but are not limited to:
- Fundamental limits of information processing: Computing and storage
- Stochastic computing, approximate computing, Shannon-inspired computing, fault-tolerant computing, error-resilient computing, and neuromorphic computing
- In-memory computing and near-data computing
- Distributed computing and storage systems
- Channel coding and signal processing for data storage
 A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proc. Annu. Conf. Neural Inf. Process. Syst. (NIPS), Dec. 2012, pp. 1097–1105.
 K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognition (CVPR), Jun. 2016, pp. 770–778.
 D. Silver et al., “Mastering the game of Go with deep neural networks and tree search,” Nature, vol. 529, no. 7587, pp. 484–489, Jan. 2016.
Dr. Yongjune Kim
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