Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

Challenges hindering memristive neuromorphic hardware from going mainstream

View through CrossRef
Abstract Memristive devices have elicited intense research in the past decade thanks to their inherent low voltage operation, multi-bit storage and cost-effective manufacturability. Nonetheless, several outstanding performance and manufacturability challenges have prevented the widespread industry adoption of redox-based memristive matrices. Here, we discuss these challenges in terms of key metrics and propose a roadmap towards realizing competitive memristive-based neuromorphic processing systems.
Title: Challenges hindering memristive neuromorphic hardware from going mainstream
Description:
Abstract Memristive devices have elicited intense research in the past decade thanks to their inherent low voltage operation, multi-bit storage and cost-effective manufacturability.
Nonetheless, several outstanding performance and manufacturability challenges have prevented the widespread industry adoption of redox-based memristive matrices.
Here, we discuss these challenges in terms of key metrics and propose a roadmap towards realizing competitive memristive-based neuromorphic processing systems.

Related Results

Neuromorphic Memristive Chips: Design and Technology
Neuromorphic Memristive Chips: Design and Technology
Memristive neuromorphic chips exploit a prospective class of novel functional materials (memristors) to deploy a new architecture of spiking neural networks for developing basic bl...
Robust analogue neuromorphic hardware networks using intrinsic physics-adaptive learning
Robust analogue neuromorphic hardware networks using intrinsic physics-adaptive learning
Abstract Analogue neuromorphic computing hardware is highly energy-efficient and has been regarded as one of the most promising technologies for advancing artificial intell...
All on board
All on board
Machine learning models offer transformative benefits across disciplines such as medicine, chemistry, and physics. However, as these models grow in size and usage, their energy dem...
Editorial: Focus on algorithms for neuromorphic computing
Editorial: Focus on algorithms for neuromorphic computing
Abstract Neuromorphic computing provides a promising energy-efficient alternative to von-Neumann-type computing and learning architectures. However, the best neuromo...
Neuromorphic computing for energy-efficient machine intelligence
Neuromorphic computing for energy-efficient machine intelligence
Abstract Neuromorphic computing has gained a significant amount of attention from industry as well as the research community as a means of overcoming the growing ...
Enabling Neuromorphic Computing for Artificial Intelligence with Hardware-Software Co-Design
Enabling Neuromorphic Computing for Artificial Intelligence with Hardware-Software Co-Design
In the last decade, neuromorphic computing was rebirthed with the emergence of novel nano-devices and hardware-software co-design approaches. With the fast advancement in algorithm...
Performance simulation methodologies for hardware/software co-designed processors
Performance simulation methodologies for hardware/software co-designed processors
Recently the community started looking into Hardware/Software (HW/SW) co-designed processors as potential solutions to move towards the less power consuming and the less complex de...
Solution-Processed Small Molecule Memristors: From Nanowire Arrays to Thin-Films
Solution-Processed Small Molecule Memristors: From Nanowire Arrays to Thin-Films
Conventional computing architectures based on the Von Neumann model are nearing their physical and operational limitations, driven by the breakdown of Moore’s law, memory bottlenec...

Back to Top