A survey on distributed machine learning
The demand for artificial intelligence has grown significantly over the past decade, and this
growth has been fueled by advances in machine learning techniques and the ability to …
growth has been fueled by advances in machine learning techniques and the ability to …
A survey on modeling and improving reliability of DNN algorithms and accelerators
S Mittal - Journal of Systems Architecture, 2020 - Elsevier
As DNNs become increasingly common in mission-critical applications, ensuring their
reliable operation has become crucial. Conventional resilience techniques fail to account for …
reliable operation has become crucial. Conventional resilience techniques fail to account for …
A hybrid dipper throated optimization algorithm and particle swarm optimization (DTPSO) model for hepatocellular carcinoma (HCC) prediction
Hepatocellular carcinoma (HCC) is a form of liver cancer that is widespread in Europe,
Africa, and Asia. The early identification of HCC is critical in improving the likelihood of …
Africa, and Asia. The early identification of HCC is critical in improving the likelihood of …
Ares: A framework for quantifying the resilience of deep neural networks
As the use of deep neural networks continues to grow, so does the fraction of compute
cycles devoted to their execution. This has led the CAD and architecture communities to …
cycles devoted to their execution. This has led the CAD and architecture communities to …
Av-fuzzer: Finding safety violations in autonomous driving systems
This paper proposes AV-FUZZER, a testing framework, to find the safety violations of an
autonomous vehicle (AV) in the presence of an evolving traffic environment. We perturb the …
autonomous vehicle (AV) in the presence of an evolving traffic environment. We perturb the …
Terminal brain damage: Exposing the graceless degradation in deep neural networks under hardware fault attacks
Deep neural networks (DNNs) have been shown to tolerate" brain damage": cumulative
changes to the network's parameters (eg, pruning, numerical perturbations) typically result in …
changes to the network's parameters (eg, pruning, numerical perturbations) typically result in …
Analyzing and increasing the reliability of convolutional neural networks on GPUs
Graphics processing units (GPUs) are playing a critical role in convolutional neural networks
(CNNs) for image detection. As GPU-enabled CNNs move into safety-critical environments …
(CNNs) for image detection. As GPU-enabled CNNs move into safety-critical environments …
EDEN: Enabling energy-efficient, high-performance deep neural network inference using approximate DRAM
The effectiveness of deep neural networks (DNN) in vision, speech, and language
processing has prompted a tremendous demand for energy-efficient high-performance DNN …
processing has prompted a tremendous demand for energy-efficient high-performance DNN …
FT-CNN: Algorithm-based fault tolerance for convolutional neural networks
Convolutional neural networks (CNNs) are becoming more and more important for solving
challenging and critical problems in many fields. CNN inference applications have been …
challenging and critical problems in many fields. CNN inference applications have been …
Pytorchfi: A runtime perturbation tool for dnns
PyTorchFI is a runtime perturbation tool for deep neural networks (DNNs), implemented for
the popular PyTorch deep learning platform. PyTorchFI enables users to perform …
the popular PyTorch deep learning platform. PyTorchFI enables users to perform …