most citedUsing the Abstract Computer Architecture Description Language to Model AI Hardware Accelerators

2 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AR2024

Efficient Edge AI: Deploying Convolutional Neural Networks on FPGA with the Gemmini Accelerator

Federico Nicolas Peccia, Svetlana Pavlitska, Tobias Fleck +1

The growing concerns regarding energy consumption and privacy have prompted the development of AI solutions deployable on the edge, circumventing the substantial CO2 emissions asso…

cs.CV2024

MR3D-Net: Dynamic Multi-Resolution 3D Sparse Voxel Grid Fusion for LiDAR-Based Collective Perception

Sven Teufel, Jörg Gamerdinger, Georg Volk +1

The safe operation of automated vehicles depends on their ability to perceive the environment comprehensively. However, occlusion, sensor range, and environmental factors limit the…

cs.CV2024

SCOPE: A Synthetic Multi-Modal Dataset for Collective Perception Including Physical-Correct Weather Conditions

Jörg Gamerdinger, Sven Teufel, Patrick Schulz +3

Collective perception has received considerable attention as a promising approach to overcome occlusions and limited sensing ranges of vehicle-local perception in autonomous drivin…

cs.AR20241 cited

A Configurable and Efficient Memory Hierarchy for Neural Network Hardware Accelerator

Oliver Bause, Paul Palomero Bernardo, Oliver Bringmann

As machine learning applications continue to evolve, the demand for efficient hardware accelerators, specifically tailored for deep neural networks (DNNs), becomes increasingly vit…

cs.AR20242 cited

Using the Abstract Computer Architecture Description Language to Model AI Hardware Accelerators

Mika Markus Müller, Alexander Richard Manfred Borst, Konstantin Lübeck +2

Artificial Intelligence (AI) has witnessed remarkable growth, particularly through the proliferation of Deep Neural Networks (DNNs). These powerful models drive technological advan…