W02 Workshop on System-level Design Methods for Deep Learning on Heterogeneous Architectures (SLOHA 2021)
Machine learning, particularly deep learning (DL), is the most quickly growing domain of artificial intelligence. DL is being applied in more and more disciplines for recognition, identification, classification, and prediction tasks. A large part of the activities is focused on image, video and speech processing, but more general signal processing tasks also benefit from DL.
Prominent application areas and markets include human-machine interaction using vocal commands, biological signals processing on wearable devices for medical and fitness applications, visual environment understanding applications such as those used in robotics and advanced driver assistance systems, or predictive maintenance in industrial automation. These applications are often embedded into a broader technical context, imposing stringent constraints on power and size. Thus, many applications should be executed on highly customized heterogeneous low-power computing platforms at the edge.
While on the one hand, there exists a large zoo of DL models and tools that target standard hardware platforms, there are many challenges when targeting heterogeneous and edge devices on the other hand. Several software and hardware design choices have to be made when developing such systems. How to automate the search for and efficiently deploy or synthesize a neural network on multiple target platforms, such as different heterogeneous low-power computing platforms and edge devices? How should the neural network look like to achieve the best possible result on a given hardware platform with limited computing power and energy budget?
The workshop's primary goal is to address these research questions and facilitate the implementation of DL applications on heterogeneous low-power computing platforms and edge devices, including accelerators such as (embedded) GPUs, FPGAs, CGRAs, and TPUs.
For further information and the workshop program, please visit: