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Found 12 papers, 1 papers with code
Abstracting Sparse DNN Acceleration via Structured Sparse Tensor Decomposition
Next, we develop a software framework, TASDER, to accelerate DNNs by searching layer-wise, high-quality structured decomposition for both weight and activation tensors so that they can be accelerated by any systems with structured sparse hardware support.
Vision Transformer Computation and Resilience for Dynamic Inference
To create dynamic models, we leverage the resilience of vision transformers to pruning and switch between different scaled versions of a model.
Zhuyi: Perception Processing Rate Estimation for Safety in Autonomous Vehicles
The processing requirement of autonomous vehicles (AVs) for high-accuracy perception in complex scenarios can exceed the resources offered by the in-vehicle computer, degrading safety and comfort.
GPU Domain Specialization via Composable On-Package Architecture
As GPUs scale their low precision matrix math throughput to boost deep learning (DL) performance, they upset the balance between math throughput and memory system capabilities.
Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles
Extracting interesting scenarios from real-world data as well as generating failure cases is important for the development and testing of autonomous systems.
Making Convolutions Resilient via Algorithm-Based Error Detection Techniques
The ability of Convolutional Neural Networks (CNNs) to accurately process real-time telemetry has boosted their use in safety-critical and high-performance computing systems.
HarDNN: Feature Map Vulnerability Evaluation in CNNs
As Convolutional Neural Networks (CNNs) are increasingly being employed in safety-critical applications, it is important that they behave reliably in the face of hardware errors.
ML-based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection
The safety and resilience of fully autonomous vehicles (AVs) are of significant concern, as exemplified by several headline-making accidents.
Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training
Further, we can enforce structured sparsity in the gate gradients to make the LSTM backward pass up to 45% faster than the state-of-the-art dense approach and 168% faster than the state-of-the-art sparsifying method on modern GPUs.
SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks
Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning.
Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks
Popular deep learning frameworks require users to fine-tune their memory usage so that the training data of a deep neural network (DNN) fits within the GPU physical memory.
vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design
The most widely used machine learning frameworks require users to carefully tune their memory usage so that the deep neural network (DNN) fits into the DRAM capacity of a GPU.
