Guest Editors:

Sankar Basu
Randal E. Bryant
Giovanni De Micheli
Thomas Theis
Lloyd Whitman

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