About this Research Topic
This Research Topic is the second volume of Research Topic "Novel Memristor-Based Devices and Circuits for Neuromorphic and AI Applications". Please see the first volume here.
Memristor (MR) devices represent a groundbreaking advancement in the field of neuromorphic and AI applications due to their unique properties, such as non volatility and multi-state capability. These devices can mimic synaptic plasticity in the brain, offering new opportunities for bio-inspired electronics that traditional silicon-based technologies cannot achieve. The history-dependent nature of MR devices allows them to transcend the limitations of von Neumann architecture, enabling In-Memory-Computing (IMC) for both digital and analog processing. This capability is particularly advantageous for energy-constrained systems like IoT and autonomous systems, where local processing and decision-making are crucial. Despite these promising attributes, there remain significant gaps in the design, fabrication, and characterization of MR devices, necessitating further research to fully harness their potential.
This research topic aims to advance the field of neuromorphic engineering by focusing on the development of novel MR devices suitable for neuromorphic applications. The primary objectives include designing, fabricating, and characterizing MR devices that can overcome the limitations of current silicon-based technologies. By leveraging the low power, high density, and high-speed operations of MR devices, the research seeks to create innovative neuromorphic architectures. The goal is to provide high-impact contributions that demonstrate the practical applications and transformative potential of MR devices in neuromorphic engineering.
To gather further insights into the deployment of MR devices for neuromorphic and AI applications, we welcome articles addressing, but not limited to, the following themes:
- Design and fabrication of novel MR stacks that mimic synaptic behavior.
- Characterization of MR devices with linear flux-charge relationships for full analog switching.
- Development of MR devices exhibiting adaptive synapse-like conductivity in response to non-voltage stimuli.
- Exploration of MR device/crossbar architectures capable of parallel computation, processing, and reliable information retention.
- Integration of MR devices in energy-constrained systems for local processing and decision-making.
Memristor (MR) devices represent a groundbreaking advancement in the field of neuromorphic and AI applications due to their unique properties, such as non volatility and multi-state capability. These devices can mimic synaptic plasticity in the brain, offering new opportunities for bio-inspired electronics that traditional silicon-based technologies cannot achieve. The history-dependent nature of MR devices allows them to transcend the limitations of von Neumann architecture, enabling In-Memory-Computing (IMC) for both digital and analog processing. This capability is particularly advantageous for energy-constrained systems like IoT and autonomous systems, where local processing and decision-making are crucial. Despite these promising attributes, there remain significant gaps in the design, fabrication, and characterization of MR devices, necessitating further research to fully harness their potential.
This research topic aims to advance the field of neuromorphic engineering by focusing on the development of novel MR devices suitable for neuromorphic applications. The primary objectives include designing, fabricating, and characterizing MR devices that can overcome the limitations of current silicon-based technologies. By leveraging the low power, high density, and high-speed operations of MR devices, the research seeks to create innovative neuromorphic architectures. The goal is to provide high-impact contributions that demonstrate the practical applications and transformative potential of MR devices in neuromorphic engineering.
To gather further insights into the deployment of MR devices for neuromorphic and AI applications, we welcome articles addressing, but not limited to, the following themes:
- Design and fabrication of novel MR stacks that mimic synaptic behavior.
- Characterization of MR devices with linear flux-charge relationships for full analog switching.
- Development of MR devices exhibiting adaptive synapse-like conductivity in response to non-voltage stimuli.
- Exploration of MR device/crossbar architectures capable of parallel computation, processing, and reliable information retention.
- Integration of MR devices in energy-constrained systems for local processing and decision-making.
Keywords: Memristor Crossbar, In-memory-computing, Synaptic electronics, Deep learning, AI Hardware, Memristor Fabrication, Smart Architecture, Memristor modelling, Accelerators, Artificial Neural Networks, Spiking Neural Networks, Parallel Processing, Nanoscale Devices
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
