5 papers
Enabling Mixed criticality applications for the Versal AI-Engines
Vincent Sprave, Martin Wilhelm, Daniele Passaretti +2
Adaptive Systems-on-Chips (SoCs) are increasingly being used in mixed criticality systems (MCSs), such as in autonomous driving, aviation and medical systems. In this context, AMD…
Rescaling-Aware Training for Efficient Deployment of Deep Learning Models on Full-Integer Hardware
Lion Mueller, Alberto Garcia-Ortiz, Ardalan Najafi +2
Integer AI inference significantly reduces computational complexity in embedded systems. Quantization-aware training (QAT) helps mitigate accuracy degradation associated with post-…
VUSA: Virtually Upscaled Systolic Array Architecture to Exploit Unstructured Sparsity in AI Acceleration
Shereef Helal, Alberto Garcia-Ortiz, Lennart Bamberg
Leveraging high degrees of unstructured sparsity is a promising approach to enhance the efficiency of deep neural network DNN accelerators - particularly important for emerging Edg…
NoCs in Heterogeneous 3D SoCs: Co-Design of Routing Strategies and Microarchitectures
Jan Moritz Joseph, Lennart Bamberg, Dominik Ermel +4
Heterogeneous 3D System-on-Chips (3D SoCs) are the most promising design paradigm to combine sensing and computing within a single chip. A special characteristic of communication n…
Area Optimization with Non-linear Models in Core Mapping for System-on-Chips
Jan Moritz Joseph, Dominik Ermel, Tobias Drewes +3
Linear models are regularly used for mapping cores to tiles in a chip. System-on-Chip (SoC) design requires integration of functional units with varying sizes, but conventional mod…