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Found 6 papers, 1 papers with code
Towards Reverse-Engineering the Brain: Brain-Derived Neuromorphic Computing Approach with Photonic, Electronic, and Ionic Dynamicity in 3D integrated circuits
The human brain has immense learning capabilities at extreme energy efficiencies and scale that no artificial system has been able to match.
Neural Sampling Machine with Stochastic Synapse allows Brain-like Learning and Inference
We experimentally show that the inherent stochastic switching of the selector element between the insulator and metallic state introduces a multiplicative stochastic noise within the synapses of NSM that samples the conductance states of the FeFET, both during learning and inference.
Inherent Weight Normalization in Stochastic Neural Networks
Multiplicative stochasticity such as Dropout improves the robustness and generalizability of deep neural networks.
Design space exploration of Ferroelectric FET based Processing-in-Memory DNN Accelerator
In this letter, we quantify the impact of device limitations on the classification accuracy of an artificial neural network, where the synaptic weights are implemented in a Ferroelectric FET (FeFET) based in-memory processing architecture.
AI based Safety System for Employees of Manufacturing Industries in Developing Countries
3. 2% alarms are coming from the changes in the system which in turn used to retrain the model and 1. 19% alarms are false alarms.
Stochastic IMT (insulator-metal-transition) neurons: An interplay of thermal and threshold noise at bifurcation
This motivates the current article where we demonstrate a stochastic neuron using an insulator-metal-transition (IMT) device, based on electrically induced phase-transition, in series with a tunable resistance.
