Deep Learning, an extremely promising instrument in the machine learning and artificial intelligence landscape. DL algorithms allow achieving very high performance in numerous applications involving recognition, identification and/or classification tasks, however, their adoption, and in general of AI technologies, is hindered by the lack of availability of low-cost and energy-efficient solutions.
Novel algorithm configurations, exploited in different domains, continuously improve the precision of DL systems. However, such advancement comes at the price of significant requirements in terms of processing power. Moreover, while the training phase is typically executed on high-performance computing facilities, recent trends of modern computing landscape push towards an ever-increasing deployment of DL inference on embedded devices. Using such an approach, according to the edge computing paradigm, DL systems may overcome limitations of cloud-based computing, when it comes to latency, bandwidth requirements, security, privacy, and availability. Nevertheless, when DL is moved at the edge, severe performance requirements must coexist with tight constraints in terms of power and energy consumption.
The ALOHA project has created a toolflow that facilitates the implementation of DL algorithms on heterogeneous low energy computing platforms. On the basis of input information such as problem definition, application constraints and description of the target processing architecture, ALOHA provides automation for key design flow stages, such as optimal algorithm selection, resource allocation and deployment.
In the ALOHA project the tool flow is associated to three use cases, that have been used to assess the capabilities of the toolflow. For each use case a demonstrator has been built and assessed within the project.
