Periodic Reporting for period 2 - ALOHA (software framework for runtime-Adaptive and secure deep Learning On Heterogeneous Architectures)
Okres sprawozdawczy: 2019-07-01 do 2021-06-30
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.
The tool is available as open source, the packages are available at the address: https://gitlab.com/aloha.eu/
In this way the tool can be exploited freely by potential users, and can be considered a key enabling instrument to foster the adoption of Deep Learning in new industry and academic projects. The tool is also exploited internally by the consortium. The development of the three reference use-case is the baseline for the creation of new products to be added to use-case providers’ portfolios. Second, tool developing companies in the consortium will support their own tool with an integrated ecosystem of other utilities that will increase their potential for customers. In general all the software and hardware companies in the consortium will improve their time to market using ALOHA in new projects to come and will be capable of providing new support to their customers.
The project results have been disseminated in multiple events for the computer science, processing architecture and embedded systems community. This has enabled the construction of an incipient user-community that is providing feedback for further improvements and new features.
The evaluation of the main key performance indicators defined to evaluate the project, assessed on the reference use-case, has demonstrated that the toolflow can reduce to days (or even to hours for simpler use-cases) the development and deployment time for CNN algorithms on embedded platforms. Such a process requires weeks or months of effort when performed manually, and requires usually very advanced skills that are hard to acquire, especially for small and medium actors on the market. ALOHA, thus, paves the way to ubiquitous adoption of CNNs. Moreover, the toolflow considers advanced aspects of the implementation, such as workload reduction, security evaluation and improvement, and adaptive management, which are not considered simultaneously by similar utilities in literature.