125 citations · 188 across the 19 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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…
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…