Nonsilicon, Non-von Neumann Computing–Part I
Volume 107, Issue 1 | January 2019
Guest Editors:
Special Issue Papers
Scanning the Issue
By S. Basu, R. E. Bryant, G. De Micheli, T. Theis, and L. Whitman
Novel Materials and Devices
The N3XT Approach to Energy-Efficient Abundant-Data Computing
By M. M. Sabry Aly, T. F. Wu, A. Bartolo, Y. H. Malviya, W. Hwang, G. Hills, I.Markov, M. Wootters, M.M. Shulaker, H.-S. P. Wong, and S.Mitra
This paper enables energy-efficient computing for transformative abundant-data applications through heterogeneous integration of energy-efficient logic devices immersed in dense nonvolatile memory, with fine-grained connectivity in a monolithic 3-D architecture.
Negative Capacitance Transistors
By J. C. Wong and S. Salahuddin
This paper provides an overview of a groundbreaking theoretical and experimental work on this promising new type of field-effect transistor.
DNA Data Storage and Hybrid Molecular–Electronic Computing
By D. Carmean, L. Ceze, G. Seelig, K. Stewart, K. Strauss, and M. Willsey
This paper attempts to address the problem of long-term storage and retrieval of large volumes of data based on emerging DNA technology.
Physics-Based Non-von Neumann Paradigm
Computing with Networks of Coupled Dynamical Systems
By A. Raychowdhury A. Parihar, G. H. Smith, V. Narayanan, G. Csaba, M. Jerry, W. Porod, and S. Datta
This paper discusses a computing architecture inspired by physics, via the radically different approach of using arrays of oscillators.
Shannon-Inspired Statistical Computing for the Nanoscale Era
By N. R. Shanbhag, N. Verma, Y. Kim, A. D. Patil, and L. R. Varshney
This paper considers a principled information-theoretic approach to the design of non-von Neumann architectures via statistical computing which leverages information-based metrics.
Neuromorphic Paradigm
The Next Generation of Deep Learning Hardware: Analog Computing
By W. Haensch, T. Gokmen, and R. Puri
This paper explores the current state of neuromorphic deep learning architectures in silicon CMOS technology.
Efficient Biosignal Processing Using Hyper-Dimensional Computing: Network Templates for Combined Learning and Classification of ExG Signals
By A. Rahimi, P. Kanerva, L. Benini, and J. M. Rabaey
This paper takes an unconventional approach to learning machines based on little explored but much promising notion of hyperdimensional computing.
Braindrop: A Mixed-Signal Neuromorphic Architecture With a Dynamical Systems-Based Programming Model
By A. Neckar, S. Fok, B. V. Benjamin, T. C. Stewart, N. N. Oza, A. R. Voelker, C. Eliasmith, R. Manohar, and K. Boahen
This paper provides an overview of a current approach for the construction of a programmable computationing machine inspired by the human brain.
CMOS and High-Performance Computing
Logic Synthesis for Established and Emerging Computing
By E. Testa, M. Soeken, L. G. Amarù, and G. De Micheli
This paper provides a state-of-the-art view on the status of logic design flows in conventional silicon CMOS as well as using several of the emerging technologies.
Customizable Computing—From Single-Chip to Datacenters
By J. Cong, Z. Fang, M. Huang, P. Wei, D. Wu, and C. H. Yu
This paper deals with the important issue of specialization in designing computing hardware that can potentially provide at least a near-term strategy to combat Moore’s law slowdown.
Architecture and Advanced Electronics Pathways Delivering Towards Highly Adaptive Energy-Efficient Computing
By G. P. Fettweis, M. Dörpinghaus, J. Castrillon, A. Kumar, C. Baier, K. Bock, F. Ellinger, A. Fery, F. H. P. Fitzek, H. Härtig, K. Jamshidi, T. Kissinger, W. Lehner, M.Mertig, W. E. Nagel, G. T. Nguyen, D. Plettemeier, M. Schröter, and T. Strufe
This paper describes a leading European effort on applications of basic technologies to energy-efficient servers and high-performance computing of the future, that has been ongoing for more than a decade.
Point of View
The Best Job in the IEEE
By H. J. Trussell
Point of View
An Outlook for Quantum Computing
By D. Maslov, Y. Nam, and J. Kim
