125 citations · 188 across the 19 of their papers we have counts for
20 papers
GPUArmor: A Hardware-Software Co-design for Efficient and Scalable Memory Safety on GPUs
Mohamed Tarek Ibn Ziad, Sana Damani, Mark Stephenson +2
Memory safety errors continue to pose a significant threat to current computing systems, and graphics processing units (GPUs) are no exception. A prominent class of memory safety a…
Kitsune: Enabling Dataflow Execution on GPUs
Michael Davies, Neal Crago, Karthikeyan Sankaralingam +1
State of art DL models are growing in size and complexity, with many modern models also increasing in heterogeneity of behavior. GPUs are still the dominant platform for DL applica…
SDQ: Sparse Decomposed Quantization for LLM Inference
Geonhwa Jeong, Po-An Tsai, Stephen W. Keckler +1
Recently, large language models (LLMs) have shown surprising performance in task-specific workloads as well as general tasks with the given prompts. However, to achieve unprecedent…
Probabilistic Tracker Management Policies for Low-Cost and Scalable Rowhammer Mitigation
Aamer Jaleel, Stephen W. Keckler, Gururaj Saileshwar
This paper focuses on mitigating DRAM Rowhammer attacks. In recent years, solutions like TRR have been deployed in DDR4 DRAM to track aggressor rows and then issue a mitigative act…
Enabling Unstructured Sparse Acceleration on Structured Sparse Accelerators
Geonhwa Jeong, Po-An Tsai, Abhimanyu R. Bambhaniya +2
Exploiting sparsity in deep neural networks (DNNs) has been a promising area for meeting the growing computation requirements. To minimize the overhead of sparse acceleration, hard…
VaPr: Variable-Precision Tensors to Accelerate Robot Motion Planning
Yu-Shun Hsiao, Siva Kumar Sastry Hari, Balakumar Sundaralingam +5
High-dimensional motion generation requires numerical precision for smooth, collision-free solutions. Typically, double-precision or single-precision floating-point (FP) formats ar…
Vision Transformer Computation and Resilience for Dynamic Inference
Kavya Sreedhar, Jason Clemons, Rangharajan Venkatesan +2
State-of-the-art deep learning models for computer vision tasks are based on the transformer architecture and often deployed in real-time applications. In this scenario, the resour…
Zhuyi: Perception Processing Rate Estimation for Safety in Autonomous Vehicles
Yu-Shun Hsiao, Siva Kumar Sastry Hari, Michał Filipiuk +5
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 saf…
GPU Domain Specialization via Composable On-Package Architecture
Yaosheng Fu, Evgeny Bolotin, Niladrish Chatterjee +2
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. We…
Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles
Zahra Ghodsi, Siva Kumar Sastry Hari, Iuri Frosio +5
Extracting interesting scenarios from real-world data as well as generating failure cases is important for the development and testing of autonomous systems. We propose efficient m…
Making Convolutions Resilient via Algorithm-Based Error Detection Techniques
Siva Kumar Sastry Hari, Michael B. Sullivan, Timothy Tsai +1
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. As su…
Estimating Silent Data Corruption Rates Using a Two-Level Model
Siva Kumar Sastry Hari, Paolo Rech, Timothy Tsai +7
High-performance and safety-critical system architects must accurately evaluate the application-level silent data corruption (SDC) rates of processors to soft errors. Such an evalu…
HarDNN: Feature Map Vulnerability Evaluation in CNNs
Abdulrahman Mahmoud, Siva Kumar Sastry Hari, Christopher W. Fletcher +7
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. T…
ML-based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection
Saurabh Jha, Subho S. Banerjee, Timothy Tsai +5
The safety and resilience of fully autonomous vehicles (AVs) are of significant concern, as exemplified by several headline-making accidents. While AV development today involves ve…
Kayotee: A Fault Injection-based System to Assess the Safety and Reliability of Autonomous Vehicles to Faults and Errors
Saurabh Jha, Timothy Tsai, Siva Hari +4
Fully autonomous vehicles (AVs), i.e., AVs with autonomy level 5, are expected to dominate road transportation in the near-future and contribute trillions of dollars to the global…
Buddy Compression: Enabling Larger Memory for Deep Learning and HPC Workloads on GPUs
Esha Choukse, Michael Sullivan, Mike O'Connor +4
GPUs offer orders-of-magnitude higher memory bandwidth than traditional CPU-only systems. However, GPU device memory tends to be relatively small and the memory capacity can not be…
Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training
Maohua Zhu, Jason Clemons, Jeff Pool +3
Exploiting sparsity enables hardware systems to run neural networks faster and more energy-efficiently. However, most prior sparsity-centric optimization techniques only accelerate…
SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks
Angshuman Parashar, Minsoo Rhu, Anurag Mukkara +6
Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning. High performance and extreme energy efficiency are critical for deployments of C…
Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks
Minsoo Rhu, Mike O'Connor, Niladrish Chatterjee +2
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. Prior w…
vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design
Minsoo Rhu, Natalia Gimelshein, Jason Clemons +2
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. This…