Aim and Scope

Event-triggered systems are gaining importance because of data abundance. While a lot of data provides better knowledge and context for systems to actuate or trigger at the right moments, the amount of useful information that characterizes an event-of-interest is typically sparse. Intelligence is key to effectively extract this information from the vast amounts of data that are being sensed, transmitted, and stored. This Special Issue targets circuits, systems, and architectures that help us exploit intelligence to extract sparse information contained in data. The SIU will cover (see topics of interest) inventive information-theoretic approaches to enable energy-efficient circuits and systems, as well as integration of these approaches with physical layer hardware using advances in machine-learning, neuromorphic computing, sparse-sampling, and hardware-friendly stochastic/machine-learning approaches.

The dramatic growth in silicon-enabled devices with projections of multi-trillion resource-constrained mobile devices in the future are giving rise to deeply interconnected systems that anticipate, adapt, and control, while being autonomous and dependable. Yet these systems find themselves unsettled in important ways. Transmitting large amounts of raw data, for example, is inefficient, clogs the network, and requires charging batteries frequently for sensor nodes (e.g., unassisted ground sensors). The problems of high-dimensional data transmission across the network and low-energy consumption can be resolved through event-triggered circuits and systems capable of autonomously maintaining required performance with high energy-efficiency. Event-triggered designs minimize run-time power consumption leveraging the context (e.g., recent events or power consumption/energy) for the specific dataset (e.g., biophysiological markers or communication spectrum). The systems also reduce energy by trading compression as a dynamic knob (against sampling frequency and computation) related to the input context. In recent years, machine/deep learning algorithms have unprecedentedly improved the accuracy of practical recognition and classification tasks, some even surpassing human-level performance. In alignment with this trend, integration of mixed-signal integrated circuits with stochastic event-driven computing can enable a new generation of computers that can be applied to a wide range of applications ranging from next-generation of communication receivers to data processing for internet-of-things.

In this context, this SI on event-triggered circuits and systems seeks research works including (but not limited to) feature-extracting radio front-ends, non-uniform-sampling, multiply-accumulate (MAC) operations, stochastic methods that consider energy- and cost-efficiency as parameters. The above challenges require a highly cross-disciplinary collective effort, as they lie at the intersection of circuits and systems, statistical signal processing, solid-state circuits, CAD, architectures, machine learning, signal processing (e.g., computer vision, audio), applied information-theory and the related communities. Accordingly, the authors of this special issue will be invited to submit their paper contributions on the following and other topics related to energy-quality scalable systems: The aim is to offer readers a clear perspective of the rich landscape of both academic and industrial endeavor in event-triggered circuits, systems and architectures from application of information-theoretic concepts to design and implementation. The special issue will not only showcase the state-of-the-art but also articulate the innovations and advances for universal adoption of such technologies in applications such as emerging sensor networks, neurocomputing, mixed/virtual reality, wearable/implantable biomedical body sensor networks, next-generation communication (5G/Beyond-5G), wireless communication networks, GHz/THz computing, and hardware security.

Topics of Interest:

 Specific topics of interest include but are not limited to, the following:

  • Non-Nyquist sampling
  • Adaptive analog-to-information converters
  • Statistical signal processing
  • Deep learning with adaptive sensing
  • Quantized compressive sensing
  • Distributed sensing and inferencing
  • On sensor, in-memory, and in-network data processing
  • Sensing, modeling, storage and transfer of sparse data
  • Asynchronous circuits (e.g., neuromorphic processors, continuous-time digital signal processing, sparse sensor data processors) 
  • Spiking neural networks and neuromorphic computing
  • Approximate computing with single and multi-class classifiers

Submission Guidelines:

Prospective authors should follow the submission guidelines for IEEE Design&Test. All manuscripts must be submitted electronically to IEEE Manuscript Central at Indicate that you are submitting your article to the special issue on "Event-triggered Circuits and Systems for Sparse Information Processing." All articles will undergo the standard IEEE Design&Test review process. Submitted manuscripts must not have been previously published or currently submitted for publication elsewhere.

Manuscripts must not exceed 5,000 words, including figures (with each average-size figure counting as 200 words) and a maximum of 12 references (50 for surveys). This amounts to about 4,000 words of text and a maximum of five small to medium figures. Accepted articles will be edited for clarity, structure, conciseness, grammar, passive to active voice, logical organization, readability, and adherence to style. Please see IEEE Design & Test Author Resources at to view links in Submission Guidelines Basics and Electronic Submission Guidelines and requirements.


  • Submission Deadline:         Mar. 31st, 2019
  • Notification First Round:   April 22nd, 2019
  • Submission of Revision:    May 20th, 2019
  • Final Notification:              June 24th, 2019
  • Final Papers Due:               July 7th, 2019

Submission Website

Guest Editors:

Please direct any questions regarding this special issue to one of the following:

Roummel Marcia

Univ. of California, Merced

[email protected]

Shuayb Zarar

Microsoft Res., Redmond, WA

[email protected]

Christoph Studer

Cornell Univ., Ithaca, NY

[email protected]

Subhanshu Gupta

Washington State, Pullman, WA

[email protected]

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