activity
20192023
most citedTDO-CIM: Transparent Detection and Offloading for Computation In-memory

12 citations · 19 across the 4 of their papers we have counts for

collaborators

5 papers

cs.PL2023

SEER: Super-Optimization Explorer for HLS using E-graph Rewriting with MLIR

Jianyi Cheng, Samuel Coward, Lorenzo Chelini +2

High-level synthesis (HLS) is a process that automatically translates a software program in a high-level language into a low-level hardware description. However, the hardware desig…

cs.AR2023★ 7 cited

CINM (Cinnamon): A Compilation Infrastructure for Heterogeneous Compute In-Memory and Compute Near-Memory Paradigms

Asif Ali Khan, Hamid Farzaneh, Karl F. A. Friebel +3

The rise of data-intensive applications exposed the limitations of conventional processor-centric von-Neumann architectures that struggle to meet the off-chip memory bandwidth dema…

cs.PL2022

MOM: Matrix Operations in MLIR

Lorenzo Chelini, Henrik Barthels, Paolo Bientinesi +3

Modern research in code generators for dense linear algebra computations has shown the ability to produce optimized code with a performance which compares and often exceeds the one…

cs.AR2020★ 12 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.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…