Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices,
and models that contrast the pervasive von Neumann computer architecture. This …

The biology-inspired intelligent computing system for the neuromorphic hardware
implementation is useful in high-speed parallel information processing. However, the …

Quantum phenomena, including entanglement, superposition, tunneling, and spin–orbit
interactions, among others, are foundational to the development of recent innovations in …

Random device mismatch that arises as a result of scaling of the CMOS (complementary
metal-oxide semiconductor) technology into the deep submicrometer regime degrades the …

The extreme learning machine (ELM) is a method to train single-layer feed-forward neural
networks that became popular because it uses a fast closed-form expression for training that …

As the integrated circuit (IC) technology advances into smaller nanometre feature sizes, a
fixed-error noise known as device mismatch is introduced owing to the dissimilarity between …

This paper aims to present a framework for supervised binary classification of n-Boolean
functions through Echo State Networks endowed with Laplacian Eigenmaps for …

Neural networks have been successfully deployed in a variety of fields like computer vision,
natural language processing, pattern recognition, etc. However most of their current …

Artificial Intelligence is becoming ubiquitous in products and services that we use daily.
Although the domain of AI has seen substantial improvements over recent years, its …

On-device intelligence is gaining significant attention recently as it offers local data
processing and low power consumption. In this research, an on-device training circuitry for …