TL;DR
The argument is presented that practically medium independent approaches to information processing, such as digital computing, are inherently less efficient than ones dependent on the specifics of implementing media, and for that reason will not have evolved.
The view taken here is that brain-inspired computing is heading toward a hybrid future: conventional digital processors will keep doing what they do best, while event-driven and in-memory accelerators take over the workloads where they have a genuine edge.
This Perspective argues that neuromorphic engineering should be defined neither by resemblance to biological components nor by any particular device, signal representation, or substrate, and develops a set of design principles for neuromorphic systems.
Findings indicate that the ACORISCVbSNN model has the potential to advance the field of bio-inspired computing, providing a highly accurate, energyefficient, and low-latency system for real-world use.
This review provides a comprehensive overview of developments in neuromorphic computing from 2019 through 2024, highlighting how co-development of hardware and algorithms is critical to fulfill the promise of neuromorphic computing, and outlining open research directions on the path toward more brain-like, efficient co...
This review systematically reviews representative 2D material systems and typical neuromorphic device architectures, establishes the mapping relationship between device characteristics and artificial neural network computation, and clarifies the advantages of 2D neuromorphic electronic devices in terms of synaptic plas...
Numerical simulations show that correlations in the stochastic weight evolution significantly affect the system response, suggesting a potential mechanism for neuromorphic-inspired computation without conventional weight training.
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