查看文章

Novel Deep Learning (DL) algorithms show ever-increasing accuracy and precision in multiple application domains. However, some steps further are needed towards the ubiquitous adoption of this kind of instrument. First, effort and skills required to develop new DL models, or to adapt existing ones to new use-cases, are hardly available for small- and medium-sized businesses. Second, DL inference must be brought at the edge, to overcome limitations posed by the classically-used cloud computing paradigm. This requires implementation on low-energy computing nodes, often heterogenous and parallel, that are usually more complex to program and to manage. This work describes the ALOHA framework, that proposes a solution to these issue by means of an integrated tool flow that automates most phases of the development process. The framework introduces architecture-awareness, considering the target …