| 1. |
- Colucci, A., et al.
(author)
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MLComp : A Methodology for Machine Learning-based Performance Estimation and Adaptive Selection of Pareto-Optimal Compiler Optimization Sequences
- 2021
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In: Proceedings -Design, Automation and Test in Europe, DATE. - : Institute of Electrical and Electronics Engineers Inc.. - 9783981926354 ; , s. 108-113
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Conference paper (peer-reviewed)abstract
- Embedded systems have proliferated in various consumer and industrial applications with the evolution of Cyber-Physical Systems and the Internet of Things. These systems are subjected to stringent constraints so that embedded software must be optimized for multiple objectives simultaneously, namely reduced energy consumption, execution time, and code size. Compilers offer optimization phases to improve these metrics. However, proper selection and ordering of them depends on multiple factors and typically requires expert knowledge. State-of-the-art optimizers facilitate different platforms and applications case by case, and they are limited by optimizing one metric at a time, as well as requiring a time-consuming adaptation for different targets through dynamic profiling. To address these problems, we propose the novel MLComp methodology, in which optimization phases are sequenced by a Reinforcement Learning-based policy. Training of the policy is supported by Machine Learning-based analytical models for quick performance estimation, thereby drastically reducing the time spent for dynamic profiling. In our framework, different Machine Learning models are automatically tested to choose the best-fitting one. The trained Performance Estimator model is leveraged to efficiently devise Reinforcement Learning-based multi-objective policies for creating quasi-optimal phase sequences. Compared to state-of-the-art estimation models, our Performance Estimator model achieves lower relative error (< 2%) with up to 50 × faster training time over multiple platforms and application domains. Our Phase Selection Policy improves execution time and energy consumption of a given code by up to 12% and 6%, respectively. The Performance Estimator and the Phase Selection Policy can be trained efficiently for any target platform and application domain. © 2021 EDAA.
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| 2. |
- Di Mauro, A., et al.
(author)
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FlyDVS : An Event-Driven Wireless Ultra-Low Power Visual Sensor Node
- 2021
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In: Proceedings -Design, Automation and Test in Europe, DATE. - : Institute of Electrical and Electronics Engineers Inc.. - 9783981926354 ; , s. 1851-1854
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Conference paper (peer-reviewed)abstract
- Event-based cameras, also called dynamic vision sensors (DVS), inspired by the human vision system, are gaining popularity due to their potential energy-saving since they generate asynchronous events only from the pixels changes in the field of view. Unfortunately, in most current uses, data acquisition, processing, and streaming of data from event-based cameras are performed by power-hungry hardware, mainly high-power FPGAs. For this reason, the overall power consumption of an event-based system that includes digital capture and streaming of events, is in the order of hundreds of milliwatts or even watts, reducing significantly usability in real-life low-power applications such as wearable devices. This work presents FlyDVS, the first event-driven wireless ultra-low-power visual sensor node that includes a low-power Lattice FPGA and, a Bluetooth wireless system-on-chip, and hosts a commercial ultra-low-power DVS camera module. Experimental results show that the low-power FPGA can reach up to 874 efps (event-frames per second) with only 17.6mW of power, and the sensor node consumes an overall power of 35.5 mW (including wireless streaming) at 200 efps. We demonstrate FlyDVS in a real-life scenario, namely, to acquire event frames of a gesture recognition data set. © 2021 EDAA.
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| 3. |
- Sadovykh, Andrey, et al.
(author)
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VeriDevOps : Automated Protection and Prevention to Meet Security Requirements in DevOps
- 2021
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In: Design, Automation and Test in Europe Conference, DATE, 2021. - 9783981926354 ; , s. 1330-1333
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Conference paper (peer-reviewed)abstract
- Current software development practices are increasingly based on using both COTS and legacy components which make such systems prone to security vulnerabilities. The modern practice addressing ever changing conditions, DevOps, promotes frequent software deliveries, however, verification methods artifacts should be updated in a timely fashion to cope with the pace of the process. VeriDevOps, Horizon 2020 project, aims at providing a faster feedback loop for verifying the security requirements and other quality attributes of large scale cyber-physical systems. VeriDevOps focuses on optimizing the security verification activities, by automatically creating verifiable models directly from security requirements formulated in natural language, using these models to check security properties on design models and then generating artefacts such as, tests or monitors that can be used later in the DevOps process. The main drivers for these advances are: Natural Language Processing, a combined formal verification and model-based testing approach, and machine-learning-based security monitors. VeriDevOps is in its initial stage - the project started on 1.10.2020 and it will run for three years. In this paper we will present the major conceptual ideas behind the project approach as well as the organizational settings.
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