Improving the robustness of analog deep neural networks through a Bayes-optimized noise injection approach
Analog deep neural networks (DNNs) provide a promising solution, especially for
deployment on resource-limited platforms, for example in mobile settings. However, the …
deployment on resource-limited platforms, for example in mobile settings. However, the …
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 …
[PDF][PDF] A Survey on Neuromorphic Architectures for Running Artificial Intelligence Algorithms
Neuromorphic computing, a brain inspired non-Von Neumann computing system, addresses
the challenges posed by the Moore's law memory wall phenomenon. It has the capability to …
the challenges posed by the Moore's law memory wall phenomenon. It has the capability to …
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 …
Programming Techniques of Resistive Random-Access Memory Devices for Neuromorphic Computing
P Machado, S Manich, Á Gómez-Pau… - Electronics, 2023 - mdpi.com
Neuromorphic computing offers a promising solution to overcome the von Neumann
bottleneck, where the separation between the memory and the processor poses increasing …
bottleneck, where the separation between the memory and the processor poses increasing …
A Design Methodology for Fault-Tolerant Neuromorphic Computing Using Bayesian Neural Network
Memristor crossbar arrays are a promising platform for neuromorphic computing. In practical
scenarios, the synapse weights represented by the memristors for the underlying system are …
scenarios, the synapse weights represented by the memristors for the underlying system are …
Programming techniques of resistive random-access memory devices for neuromorphic computing
P Machado Panadés, S Manich Bou… - Electronics …, 2023 - upcommons.upc.edu
Neuromorphic computing offers a promising solution to overcome the von Neumann
bottleneck, where the separation between the memory and the processor poses increasing …
bottleneck, where the separation between the memory and the processor poses increasing …
Machine learning-based predictive dynamics for vehicle motion control
S Zeng, Y Zhang, BB Litkouhi - US Patent App. 18/049,809, 2024 - Google Patents
A method includes receiving sensed vehicle-state data, actuation-command data, and
surface-coefficient data from a plurality of remote vehicles, inputting the sensed vehicle-state …
surface-coefficient data from a plurality of remote vehicles, inputting the sensed vehicle-state …