The IEEE Transactions on Nanotechnology (TNANO) seeks original manuscripts for a Special Section on: “Neuromorphic Computing”

The explosive growth in data-centric computing-related applications necessitates a radical departure from traditional von Neumann computing systems. These systems involve separate processing and memory units that lead to significant latency, bandwidth, and energy consumption in the information transfer between the processing and memory units. Neuromorphic computing has emerged as one of the solutions that potentially overcomes the von Neumann bottleneck. It aims to facilitate the highest computing speeds while reducing the need for bulky devices and power-consuming dedicated computing systems. Neuromorphic computing is expected to solve most of the artificial intelligence’s (AI’s) current issues while opening new perspectives in the decades to come. It is getting tremendous attention from companies like Intel, IBM, Applied Brain Research, Brain Corporation, Hewlett Packard Enterprise, Samsung Electronics, and Qualcomm, putting their might behind it. According to a US-based Research and Markets report, the neuromorphic computing market is poised to increase over the next decade to reach approximately $1.78 billion by 2025. Despite the remarkable strides made in this field recently, several hurdles need to be overcome at the device level and scale up neuromorphic systems to sizes that enable useful applications.

This Special section of the IEEE Transactions on Nanotechnology solicits contributed articles that will report the most recent developments in the field of neuromorphic computing with a view to overcoming the von Neumann bottleneck and the exorbitant energy consumption associated with information processing. This section will also serve as the basis to chart out future research directions. Topics of interest include, but are not limited to:

  • Device, circuit, architecture design, analysis and optimization for neuromorphic computing systems
  • Mathematical modeling of neural systems
  • Emerging materials for devices of neuromorphic computing importance
  • Device to system level framework and optimization
  • Hardware accelerators for machine/deep learning algorithms
  • Multi-scale neural systems
  • On-chip learning and inference
  • Learning algorithms and optimizations
  • Mapping algorithms
  • Complexity and scalability of neuromorphic computing
  • Reliability and security in neuromorphic computing
  • Emerging technologies for brain-inspired nano-computing and communication
  • Applications of neuromorphic computing in embedded and IoT devices, unmanned vehicles and drones, and cyber-physical systems

Follow the guideline (, and submit your paper to ScholarOne Manuscripts at, indicating in the cover letter that you wish the paper to be considered for “IEEE Transactions on Nanotechnology (TNANO) Special Section on “Neuromorphic Computing”Please note that the type of submissions is Regular Manuscripts, i.e., 4 to 8 pages in the two-column IEEE format, which includes figures, tables, and references. On submission to TNANO, authors should select the “Special Issue” manuscript type instead of “Regular Paper”.

Manuscripts will be subject to the standard competitive and constructive peer-review TNANO criteria with no article publishing charges. Accepted papers are published on the web in IEEE Xplore as soon as they are submitted in final form. Web-published papers have a DOI (Digital Object Identifier), and are fully citable and downloadable.

Important Dates

  • Submission deadline: September 30, 2021
  • First decision (accept/reject/revise): December 31, 2021
  • Revised papers submission: February 27, 2022
  • Final decision: April 30, 2022

Guest Editors (Alphabetical Order)

  • Amit Ranjan Trivedi, University of Illinois Chicago, USA, Email:
  • Brajesh Kumar Kaushik, Indian Institute of Technology, Roorkee, India, Email: (Leading Guest Editor)
  • Giovanni Finocchio, University of Messina, Italy, Email:
  • Supriyo Bandyopadhyay, Virginia Commonwealth University, USA, Email:
  • Yufei Ding, University of California-Santa Barbara, USA, Email:

Responsible T-NANO Senior Editor: Prof. Sorin Cotofana