Two-dimensional materials-based probabilistic synapses and reconfigurable neurons for measuring inference uncertainty using Bayesian neural networks
oleh: Amritanand Sebastian, Rahul Pendurthi, Azimkhan Kozhakhmetov, Nicholas Trainor, Joshua A. Robinson, Joan M. Redwing, Saptarshi Das
| Format: | Article |
|---|---|
| Diterbitkan: | Nature Portfolio 2022-10-01 |
Deskripsi
Designing efficient Bayesian neural networks remains a challenge. Here, the authors use the cycle variation in the programming of the 2D memtransistors to achieve Gaussian random number generator-based synapses, and combine it with the complementary 2D memtransistors-based tanh function to implement a Bayesian neural network.