activity
20212026
most citedCINM (Cinnamon): A Compilation Infrastructure for Heterogeneous Compute In-Memory and Compute Near-Memory Paradigms

7 citations · 14 across the 5 of their papers we have counts for

collaborators

5 papers

cs.PL2026

Demonstrating a Future for MLIR-native DSL Compilers on a NumPy-like Example

Karl F. A. Friebel, Jascha A. Ohlmann, Jeronimo Castrillon

Compilers for general-purpose languages have been shown to be at a disadvantage when it comes to specialized application domains as opposed to their Domain-Specific Language (DSL)…

cs.AR2024

A System Development Kit for Big Data Applications on FPGA-based Clusters: The EVEREST Approach

Christian Pilato, Subhadeep Banik, Jakub Beranek +28

Modern big data workflows are characterized by computationally intensive kernels. The simulated results are often combined with knowledge extracted from AI models to ultimately sup…

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.AR2022★ 1 cited

Automatic Creation of High-Bandwidth Memory Architectures from Domain-Specific Languages: The Case of Computational Fluid Dynamics

Stephanie Soldavini, Karl F. A. Friebel, Mattia Tibaldi +3

Numerical simulations can help solve complex problems. Most of these algorithms are massively parallel and thus good candidates for FPGA acceleration thanks to spatial parallelism.…

cs.DC2021★ 6 cited

From Domain-Specific Languages to Memory-Optimized Accelerators for Fluid Dynamics

Karl F. A. Friebel, Stephanie Soldavini, Gerald Hempel +2

Many applications are increasingly requiring numerical simulations for solving complex problems. Most of these numerical algorithms are massively parallel and often implemented on…