In the study, the team programmed a Bayesian neural network on an assembly of 75 crossbar arrays, each containing 1,024 memristors, complemented by CMOS periphery for in-memory computing. The experiment involved a two-layer Bayesian neural network trained to differentiate nine classes of heart arrhythmia from electrocardiogram (ECG) recordings.
The experimental results were compelling, with the memristor-based Bayesian neural network nearly matching the best performances of a software neural network in terms of both aleatoric (area under the curve of 0.91) and epistemic uncertainty evaluation (area under the curve of 0.99), albeit with a slight reduction in raw accuracy. This performance sharply contrasted conventional neural networks, which exhibit little ability to recognize unknown data and evaluate uncertainties.
That’s amazing stuff! Now add these new memristors to quantum computers , LLM’s and AI. Bam!
