activity
20192022
most citedFPGA-Based Near-Memory Acceleration of Modern Data-Intensive Applications

74 citations · 93 across the 6 of their papers we have counts for

collaborators

8 papers

q-bio.GN2022

Going From Molecules to Genomic Variations to Scientific Discovery: Intelligent Algorithms and Architectures for Intelligent Genome Analysis

Mohammed Alser, Joel Lindegger, Can Firtina +5

We now need more than ever to make genome analysis more intelligent. We need to read, analyze, and interpret our genomes not only quickly, but also accurately and efficiently enoug…

cs.AR202174 cited

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…

cs.AR2020

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…

cs.AR202012 cited

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…

cs.DC2020

Agile Autotuning of a Transprecision Tensor Accelerator Overlay for TVM Compiler Stack

Dionysios Diamantopoulos, Burkhard Ringlein, Mitra Purandare +2

Specialized accelerators for tensor-operations, such as blocked-matrix operations and multi-dimensional convolutions, have been emerged as powerful architecture choices for high-pe…

cs.AR2019

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…