This scenario refers to Smart Industry, where Deep Learning is used for speech recognition. The objective of this use case is to develop an embedded speech recognition system that would activate/deactivate PLC-controlled tooling machinery or collaborative robot in an industrial environment, without relying on a cloud backend.
This scenario refers to Critical Infrastructures surveillance, where Deep Learning is meant to be used to define an intelligent video-based detection system for security in and around the critical infrastructure. A specific challenge is to use deep learning methods to enable technician after a short training to utilize advanced capabilities of modern surveillance techniques.
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This use-case refers to a DL-based smart assistant, which supports emergency room situations, identifying acute intracranial bleeds in non-contrast CT images. The consortium wants to assess the benefits provided by the ALOHA tool flow within the development of an embedded medical decision assistant, considering the requirements posed by the specific application in terms of accuracy and performance.
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The features of the architecture that will execute the inference are taken into account during the whole development process, starting from the early stages such as pre-training hyperparameter optimization and algorithm configuration.
The tool flow implements support for agile development methodologies, to be easily adopted by SMEs and midcaps.
The development process considers that the system should adapt to different operating modes at runtime.
The development process is conceived to support novel processing platforms to be exploitable beyond the end of the project.
The development process automates the introduction of algorithm features and programming techniques improving the resilience of the system to attacks.
In DL, good precision levels can be also obtained using algorithms with reduced complexity. All the optimization utilities in the ALOHA tool flow will consider the effects of algorithm simplification on precision, execution time, energy and power.
In this video, speech recognition is used to control a robotic arm (e.Do by Comau) keeping hands free. Two modalities are available: predefined poses or spatial mode using x, y and z coordinates. The deep neural network model is speaker independent and classifies audio into ten keywords.
Video by Santer Reply.
The HiPEAC conference (High Performance and Embedded Architecture and Compilation) is a prestigious annual European forum for experts in computer architecture, programming models, compilers and operating systems for embedded and general-purpose systems. The HiPEAC 2020 conference will take place in Bologna, Italy (Polo congressuale).
The ALOHA consortium will held a tutorial during HiPEAC 2020, on January 21th, SALA AVORIO (from 2:00 p.m. to 5:30 p.m.). The tutorial is meant to offer an overview how to use the ALOHA framework and its different functionalities.
The video presents the initial version of the smart industry demonstrator, where Deep Learning is used for speech recognition. Santer Reply, responsible for this this use case, realized, using the ALOHA toolflow, an embedded keyword spotting system to control a robot without relying on a cloud backend.
Video by Cristina Chesta, Santer Reply.