Supervised learning in all FeFET-based spiking neural network: Opportunities and challenges
The two possible pathways toward artificial intelligence (AI)—(i) neuroscience-oriented
neuromorphic computing [like spiking neural network (SNN)] and (ii) computer science …
neuromorphic computing [like spiking neural network (SNN)] and (ii) computer science …
Neural sampling machine with stochastic synapse allows brain-like learning and inference
Many real-world mission-critical applications require continual online learning from noisy
data and real-time decision making with a defined confidence level. Brain-inspired …
data and real-time decision making with a defined confidence level. Brain-inspired …
Autonomous probabilistic coprocessing with petaflips per second
In this article we present a concrete design for a probabilistic (p-) computer based on a
network of p-bits, robust classical entities fluctuating between− 1 and+ 1, with probabilities …
network of p-bits, robust classical entities fluctuating between− 1 and+ 1, with probabilities …
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 …
Binary‐Stochasticity‐Enabled Highly Efficient Neuromorphic Deep Learning Achieves Better‐than‐Software Accuracy
In this work, the requirement of using high‐precision (HP) signals is lifted and the circuits for
implementing deep learning algorithms in memristor‐based hardware are simplified. The …
implementing deep learning algorithms in memristor‐based hardware are simplified. The …
Locally learned synaptic dropout for complete bayesian inference
The Bayesian brain hypothesis postulates that the brain accurately operates on statistical
distributions according to Bayes' theorem. The random failure of presynaptic vesicles to …
distributions according to Bayes' theorem. The random failure of presynaptic vesicles to …