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Related publications (33)
Exploring High-Performance and Energy-Efficient Architectures for Edge AI-Enabled Applications
Joshua Alexander Harrison Klein
The desire and ability to place AI-enabled applications on the edge has grown significantly in recent years. However, the compute-, area-, and power-constrained nature of edge devices are stressed by the needs of the AI-enabled applications, due to a gener ...
EPFL2024Acceleration of Control Intensive Applications on Coarse-Grained Reconfigurable Arrays for Embedded Systems
David Atienza Alonso, Miguel Peon Quiros, Benoît Walter Denkinger
Embedded systems confront two opposite goals: low-power operation and high performance. The current trend to reach these goals is toward heterogeneous platforms, including multi-core architectures with heterogeneous cores and hardware accelerators. The lat ...
2023SIMD Parallel Execution on GPU from High-Level Dataflow Synthesis
Marco Mattavelli, Simone Casale Brunet, Aurélien François Gilbert Bloch
Writing and optimizing application software for heterogeneous platforms including GPU units is a very difficult task that requires designer efforts and resources to consider several key elements to obtain good performance. Dataflow programming has shown to ...
2022DFAulted: Analyzing and Exploiting CPU Software Faults Caused by FPGA-Driven Undervolting Attacks
Mirjana Stojilovic, Dina Gamaleldin Ahmed Shawky Mahmoud, David Dervishi
Field-programmable gate arrays (FPGAs) combine hardware reconfigurability with a high degree of parallelism. Consequently, FPGAs offer performance gains and power savings for many applications. A recent trend has been to leverage the hardware versatility o ...
2022A novel assessment framework for learning-based deepfake detectors in realistic conditions
Detecting manipulations in facial images and video has become an increasingly popular topic in media forensics community. At the same time, deep convolutional neural networks have achieved exceptional results on deepfake detection tasks. Despite the remark ...
2022Assessing the Efficacy of a GPU-Based MW-FDTD Method for Calculating Lightning Electromagnetic Fields Over Large-Scale Terrains
Marcos Rubinstein, Mohammad Azadifar, Farhad Rachidi-Haeri, Hamidreza Karami
This article presents for the first time an OpenACC (Open accelerators)-aided Graphics Processing Unit (GPU)-based approach adopting a 3D moving window finite difference time domain (MW-FDTD) method for calculating lightning electromagnetic fields over lar ...
2020On the Efficiency of OpenACC-aided GPU-Based FDTD Approach: Application to Lightning Electromagnetic Fields
Mohammad Azadifar, Hamidreza Karami
An open accelerator (OpenACC)-aided graphics processing unit (GPU)-based finite difference time domain (FDTD) method is presented for the first time for the 3D evaluation of lightning radiated electromagnetic fields along a complex terrain with arbitrary t ...
MDPI2020