Hardware-Aware Layer Fusion of Deep Neural Networks
Beschreibung
Dataflow and mapping of Convolutional Neural Networks (CNN) influences their compute and energy efficiency on edge accelerators. Layer fusion is a concept which enables the processing of multiple CNN layers without resorting to costly off-chip memory accesses. In order to optimally implement layer fusion, different combinations of mapping and scheduling parameters need to be explored. We, at the BMW group, offer you a challenging master thesis position that aims to optimize the fusion strategy of a given CNN workload for maximal data reuse and resource utilization.
Voraussetzungen
- Strong knowledge in computer vision concepts, and convolutional neural networks.
- Hands-on experience with Xilinx FPGAs, Verilog/VHDL/HLS.
- Excellent programming skills in C, Python. Experience in Tensorflow 2, Git, Docker is a plus.
- Highly motivated and eager to collaborate in a team.
- Ability to speak and write in English fluently.
Kontakt
Shambhavi.balamuthu-sampath@bmw.de