ALOHA

The main goal of external pageALOHAcall_made is to facilitate implementation of deep learning on heterogeneous low-energy computing platforms. To this aim, the project will develop a software development tool flow, automating:

• algorithm design and analysis;
  
• porting of the inference tasks to heterogeneous embedded architectures, with optimized mapping and scheduling;
  
• mplementation of middleware and primitives controlling the target platform, to optimize power and energy savings.

During the development of the external pageALOHAcall_made tool flow, several main features will be addressed, such as architecture-awareness (the features of the embedded architecture will be considered starting from the algorithm design), adaptivity, security, productivity, and extensibility. external pageALOHAcall_made will be assessed over three different use-cases, involving surveillance, smart industry automation, and medical application domains.

The Digital Circuits and Systems Group  of ETH Zurich, will be directly involved in neural network quantization and approximation for energy-efficient inference as part of this project.