74 citations · 232 across the 14 of their papers we have counts for
10 papers · 1 filter
SPARTA: Spatial Acceleration for Efficient and Scalable Horizontal Diffusion Weather Stencil Computation
Gagandeep Singh, Alireza Khodamoradi, Kristof Denolf +6
Fast and accurate climate simulations and weather predictions are critical for understanding and preparing for the impact of climate change. Real-world weather and climate modeling…
An Experimental Evaluation of Machine Learning Training on a Real Processing-in-Memory System
Juan Gómez-Luna, Yuxin Guo, Sylvan Brocard +5
Training machine learning (ML) algorithms is a computationally intensive process, which is frequently memory-bound due to repeatedly accessing large training datasets. As a result,…
SeGraM: A Universal Hardware Accelerator for Genomic Sequence-to-Graph and Sequence-to-Sequence Mapping
Damla Senol Cali, Konstantinos Kanellopoulos, Joel Lindegger +15
A critical step of genome sequence analysis is the mapping of sequenced DNA fragments (i.e., reads) collected from an individual to a known linear reference genome sequence (i.e.,…
Sibyl: Adaptive and Extensible Data Placement in Hybrid Storage Systems Using Online Reinforcement Learning
Gagandeep Singh, Rakesh Nadig, Jisung Park +7
Hybrid storage systems (HSS) use multiple different storage devices to provide high and scalable storage capacity at high performance. Recent research proposes various techniques t…
Casper: Accelerating Stencil Computation using Near-cache Processing
Alain Denzler, Rahul Bera, Nastaran Hajinazar +4
Stencil computation is one of the most used kernels in a wide variety of scientific applications, ranging from large-scale weather prediction to solving partial differential equati…
FPGA-Based Near-Memory Acceleration of Modern Data-Intensive Applications
Gagandeep Singh, Mohammed Alser, Damla Senol Cali +4
Modern data-intensive applications demand high computation capabilities with strict power constraints. Unfortunately, such applications suffer from a significant waste of both exec…
Accelerating Weather Prediction using Near-Memory Reconfigurable Fabric
Gagandeep Singh, Dionysios Diamantopoulos, Juan Gómez-Luna +4
Ongoing climate change calls for fast and accurate weather and climate modeling. However, when solving large-scale weather prediction simulations, state-of-the-art CPU and GPU impl…
NERO: A Near High-Bandwidth Memory Stencil Accelerator for Weather Prediction Modeling
Gagandeep Singh, Dionysios Diamantopoulos, Christoph Hagleitner +4
Ongoing climate change calls for fast and accurate weather and climate modeling. However, when solving large-scale weather prediction simulations, state-of-the-art CPU and GPU impl…
TDO-CIM: Transparent Detection and Offloading for Computation In-memory
Kanishkan Vadivel, Lorenzo Chelini, Ali BanaGozar +4
Computation in-memory is a promising non-von Neumann approach aiming at completely diminishing the data transfer to and from the memory subsystem. Although a lot of architectures h…
Near-Memory Computing: Past, Present, and Future
Gagandeep Singh, Lorenzo Chelini, Stefano Corda +5
The conventional approach of moving data to the CPU for computation has become a significant performance bottleneck for emerging scale-out data-intensive applications due to their…