56 citations · 105 across the 5 of their papers we have counts for
6 papers
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…
DeLTA: GPU Performance Model for Deep Learning Applications with In-depth Memory System Traffic Analysis
Sangkug Lym, Donghyuk Lee, Mike O'Connor +2
Training convolutional neural networks (CNNs) requires intense compute throughput and high memory bandwidth. Especially, convolution layers account for the majority of the executio…
What Your DRAM Power Models Are Not Telling You: Lessons from a Detailed Experimental Study
Saugata Ghose, Abdullah Giray Yağlıkçı, Raghav Gupta +9
Main memory (DRAM) consumes as much as half of the total system power in a computer today, resulting in a growing need to develop new DRAM architectures and systems that consume le…
Voltron: Understanding and Exploiting the Voltage-Latency-Reliability Trade-Offs in Modern DRAM Chips to Improve Energy Efficiency
Kevin K. Chang, Abdullah Giray Yaglıkçı, Saugata Ghose +7
This paper summarizes our work on experimental characterization and analysis of reduced-voltage operation in modern DRAM chips, which was published in SIGMETRICS 2017, and examines…
Understanding Reduced-Voltage Operation in Modern DRAM Chips: Characterization, Analysis, and Mechanisms
Kevin K. Chang, Abdullah Giray Yağlıkçı, Saugata Ghose +7
The energy consumption of DRAM is a critical concern in modern computing systems. Improvements in manufacturing process technology have allowed DRAM vendors to lower the DRAM suppl…
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…