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Found 10 papers, 1 papers with code
Network architecture search of X-ray based scientific applications
Our NAS and HPS of (1) BraggNN achieves a 31. 03\% improvement in bragg peak detection accuracy with a 87. 57\% reduction in model size, and (2) PtychoNN achieves a 16. 77\% improvement in model accuracy and a 12. 82\% reduction in model size when compared to the baseline PtychoNN model.
AudioFool: Fast, Universal and synchronization-free Cross-Domain Attack on Speech Recognition
Automatic Speech Recognition systems have been shown to be vulnerable to adversarial attacks that manipulate the command executed on the device.
Low Precision Quantization-aware Training in Spiking Neural Networks with Differentiable Quantization Function
Deep neural networks have been proven to be highly effective tools in various domains, yet their computational and memory costs restrict them from being widely deployed on portable devices.
Thermal Heating in ReRAM Crossbar Arrays: Challenges and Solutions
The higher speed, scalability and parallelism offered by ReRAM crossbar arrays foster development of ReRAM-based next generation AI accelerators.
Mixed-Precision Neural Networks: A Survey
Mixed-precision Deep Neural Networks achieve the energy efficiency and throughput needed for hardware deployment, particularly when the resources are limited, without sacrificing accuracy.
DT2CAM: A Decision Tree to Content Addressable Memory Framework
Decision trees are considered one of the most powerful tools for data classification.
Configurable Independent Component Analysis Preprocessing Accelerator
Specifically, the proposed accelerator is based on a high-performance matrix multiplication array (MMA).
Efficient Training of Spiking Neural Networks with Temporally-Truncated Local Backpropagation through Time
The results reveal that temporal truncation has a negative effect on the accuracy of classifying frame-based datasets, but leads to improvement in accuracy on dynamic-vision-sensor (DVS) recorded datasets.
Resistive Neural Hardware Accelerators
The review covers different aspects of hardware and software realization of DNN accelerators, their present limitations, and future prospectives.
On-Chip Error-triggered Learning of Multi-layer Memristive Spiking Neural Networks
Recent breakthroughs in neuromorphic computing show that local forms of gradient descent learning are compatible with Spiking Neural Networks (SNNs) and synaptic plasticity.
