Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks
Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from
limited, noisy data. Bayesian neural networks excel at such tasks, offering predictive …
limited, noisy data. Bayesian neural networks excel at such tasks, offering predictive …
Spintronics for achieving system-level energy-efficient logic
The demand for data processing in high-performance computing is growing rapidly.
Extrapolating these trends to the long term suggests that a switch, which is more energy …
Extrapolating these trends to the long term suggests that a switch, which is more energy …
[HTML][HTML] Roadmap to neuromorphic computing with emerging technologies
The growing adoption of data-driven applications, such as artificial intelligence (AI), is
transforming the way we interact with technology. Currently, the deployment of AI and …
transforming the way we interact with technology. Currently, the deployment of AI and …
Stochastic domain wall-magnetic tunnel junction artificial neurons for noise-resilient spiking neural networks
The spatiotemporal nature of neuronal behavior in spiking neural networks (SNNs) makes
SNNs promising for edge applications that require high energy efficiency. To realize SNNs …
SNNs promising for edge applications that require high energy efficiency. To realize SNNs …
Spintronic Artificial Neurons Showing Integrate-and-Fire Behavior with Reliable Cycling Operation
The rich dynamics of magnetic materials makes them promising candidates for neural
networks that, like the brain, take advantage of dynamical behaviors to efficiently compute …
networks that, like the brain, take advantage of dynamical behaviors to efficiently compute …
Thermodynamic computing system for AI applications
Recent breakthroughs in artificial intelligence (AI) algorithms have highlighted the need for
novel computing hardware in order to truly unlock the potential for AI. Physics-based …
novel computing hardware in order to truly unlock the potential for AI. Physics-based …
Probabilistic photonic computing with chaotic light
Biological neural networks effortlessly tackle complex computational problems and excel at
predicting outcomes from noisy, incomplete data. Artificial neural networks (ANNs), inspired …
predicting outcomes from noisy, incomplete data. Artificial neural networks (ANNs), inspired …
Memristive Monte Carlo DropConnect crossbar array enabled by device and algorithm co-design
Device and algorithm co-design aims to develop energy-efficient hardware that directly
implements complex algorithms and optimizes algorithms to match the hardware's …
implements complex algorithms and optimizes algorithms to match the hardware's …
Graphene-based artificial dendrites for bio-Inspired learning in spiking neuromorphic systems
Analog neuromorphic computing systems emulate the parallelism and connectivity of the
human brain, promising greater expressivity and energy efficiency compared to those of …
human brain, promising greater expressivity and energy efficiency compared to those of …
Spinbayes: Algorithm-hardware co-design for uncertainty estimation using bayesian in-memory approximation on spintronic-based architectures
Recent development in neural networks (NNs) has led to their widespread use in critical and
automated decision-making systems, where uncertainty estimation is essential for …
automated decision-making systems, where uncertainty estimation is essential for …