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 …
A memristor-based Bayesian machine
Memristors, and other emerging memory technologies, can be used to create energy-
efficient implementations of neural networks. However, for certain edge applications (in …
efficient implementations of neural networks. However, for certain edge applications (in …
Improving realistic worst-case performance of NVCiM DNN accelerators through training with right-censored gaussian noise
Compute-in-Memory (CiM), built upon non-volatile memory (NVM) devices, is promising for
accelerating deep neural networks (DNNs) owing to its in-situ data processing capability …
accelerating deep neural networks (DNNs) owing to its in-situ data processing capability …
Computing-in-memory neural network accelerators for safety-critical systems: Can small device variations be disastrous?
Computing-in-Memory (CiM) architectures based on emerging nonvolatile memory (NVM)
devices have demonstrated great potential for deep neural network (DNN) acceleration …
devices have demonstrated great potential for deep neural network (DNN) acceleration …
Reliable memristor-based neuromorphic design using variation-and defect-aware training
The memristor crossbar provides a unique opportunity to develop a neuromorphic
computing system (NCS) with high scalability and energy efficiency. However, the reliability …
computing system (NCS) with high scalability and energy efficiency. However, the reliability …
Enhancing reliability of neural networks at the edge: Inverted normalization with stochastic affine transformations
Bayesian Neural Networks (BayNNs) naturally provide uncertainty in their predictions,
making them a suitable choice in safety-critical applications. Additionally, their realization …
making them a suitable choice in safety-critical applications. Additionally, their realization …
Brocom: A bayesian framework for robust computing on memristor crossbar
Memristor crossbar arrays are considered to be a promising platform for neuromorphic
computing. To deploy a trained neural network (NN) model on memristor crossbars …
computing. To deploy a trained neural network (NN) model on memristor crossbars …
AD2VNCS: Adversarial Defense and Device Variation-tolerance in Memristive Crossbar-based Neuromorphic Computing Systems
In recent years, memristive crossbar-based neuromorphic computing systems (NCS) have
obtained extremely high performance in neural network acceleration. However, adversarial …
obtained extremely high performance in neural network acceleration. However, adversarial …
On the reliability of computing-in-memory accelerators for deep neural networks
Computing-in-memory with emerging non-volatile memory (nvCiM) is shown to be a
promising candidate for accelerating deep neural networks (DNNs) with high energy …
promising candidate for accelerating deep neural networks (DNNs) with high energy …
Negative Feedback Training: A Novel Concept to Improve Robustness of NVCiM DNN Accelerators
Compute-in-memory (CIM) accelerators built upon non-volatile memory (NVM) devices
excel in energy efficiency and latency when performing Deep Neural Network (DNN) …
excel in energy efficiency and latency when performing Deep Neural Network (DNN) …