173 citations · 782 across the 61 of their papers we have counts for
9 papers · 1 filter
Exploiting Row-Level Temporal Locality in DRAM to Reduce the Memory Access Latency
Hasan Hassan, Gennady Pekhimenko, Nandita Vijaykumar +4
This paper summarizes the idea of ChargeCache, which was published in HPCA 2016 [51], and examines the work's significance and future potential. DRAM latency continues to be a crit…
SoftMC: Practical DRAM Characterization Using an FPGA-Based Infrastructure
Hasan Hassan, Nandita Vijaykumar, Samira Khan +6
This paper summarizes the SoftMC DRAM characterization infrastructure, which was published in HPCA 2017, and examines the work's significance and future potential. SoftMC (Soft Mem…
Flexible-Latency DRAM: Understanding and Exploiting Latency Variation in Modern DRAM Chips
Kevin K. Chang, Abhijith Kashyap, Hasan Hassan +7
This article summarizes key results of our work on experimental characterization and analysis of latency variation and latency-reliability trade-offs in modern DRAM chips, which wa…
RowClone: Accelerating Data Movement and Initialization Using DRAM
Vivek Seshadri, Yoongu Kim, Chris Fallin +8
In existing systems, to perform any bulk data movement operation (copy or initialization), the data has to first be read into the on-chip processor, all the way into the L1 cache,…
Adaptive-Latency DRAM: Reducing DRAM Latency by Exploiting Timing Margins
Donghyuk Lee, Yoongu Kim, Gennady Pekhimenko +4
This paper summarizes the idea of Adaptive-Latency DRAM (AL-DRAM), which was published in HPCA 2015, and examines the work's significance and future potential. AL-DRAM is a mechani…
Decoupling GPU Programming Models from Resource Management for Enhanced Programming Ease, Portability, and Performance
Nandita Vijaykumar, Kevin Hsieh, Gennady Pekhimenko +6
The application resource specification--a static specification of several parameters such as the number of threads and the scratchpad memory usage per thread block--forms a critica…
Echo: Compiler-based GPU Memory Footprint Reduction for LSTM RNN Training
Bojian Zheng, Abhishek Tiwari, Nandita Vijaykumar +1
The Long-Short-Term-Memory Recurrent Neural Networks (LSTM RNNs) are a popular class of machine learning models for analyzing sequential data. Their training on modern GPUs, howeve…
TBD: Benchmarking and Analyzing Deep Neural Network Training
Hongyu Zhu, Mohamed Akrout, Bojian Zheng +4
The recent popularity of deep neural networks (DNNs) has generated a lot of research interest in performing DNN-related computation efficiently. However, the primary focus is usual…
Zorua: Enhancing Programming Ease, Portability, and Performance in GPUs by Decoupling Programming Models from Resource Management
Nandita Vijaykumar, Kevin Hsieh, Gennady Pekhimenko +5
The application resource specification--a static specification of several parameters such as the number of threads and the scratchpad memory usage per thread block--forms a critica…