Optimized Hardware Vision System for Vehicle Detection based on FPGA and Combining Machine Learning and PSO

Hamdaoui, Fayçal; Bougharriou, Sana; Mtibaa, Abdellatif

doi:10.1016/j.micpro.2022.104469

Cited by 4 publications

(2 citation statements)

References 33 publications

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“…The workflow and results detailed throughout Section 3 exemplify the importance of the Safety ArtISt method, along with the dynamics of its steps, activities, and recommended practices, in iteratively building evidence-based arguments that allow ascertaining whether a system meets its qualitative and quantitative safety targets with the aid of analyses, simulations, and physical experiments. Hence, it goes beyond pre-existing research efforts involving safety-critical systems with FPGA-based AI, whose focus is on detailing AI design and functional performance while falling short of demonstrating full compliance with safety requirements that take into account both operational and long-term fault tolerance aspects [42][43][44][45][46][47].…”

Section: Discussionmentioning

confidence: 99%

“…specific safety constraints that are applicable to safety-critical systems with FPGAs. For instance, the majority of research involving safety, AI, and FPGAs altogether is solely based on presenting FPGA-based solutions for implementing safety-critical AI, notably in vehicle control automation [42][43][44] and general-purpose building blocks for FPGA design in safetycritical systems [45][46][47]. Therefore, these efforts lack safety assurance aspects, such as analyses that support whether safety requirements have indeed been met.…”

Section: Introductionmentioning

confidence: 99%

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Design and Assurance of Safety-Critical Systems with Artificial Intelligence in FPGAs: The Safety ArtISt Method and a Case Study of an FPGA-Based Autonomous Vehicle Braking Control System

Silva Neto,

Silva,

Camargo

et al. 2023

Electronics

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With the advancements in utilizing Artificial Intelligence (AI) in embedded safety-critical systems based on Field-Programmable Gate Arrays (FPGAs), assuring that these systems meet their safety requirements is of paramount importance for their revenue service. Based on this context, this paper has two main objectives. The first of them is to present the Safety ArtISt method, developed by the authors to guide the lifecycle of AI-based safety-critical systems, and emphasize its FPGA-oriented tasks and recommended practice towards safety assurance. The second one is to illustrate the application of Safety ArtISt with an FPGA-based braking control system for autonomous vehicles relying on explainable AI generated with High-Level Synthesis. The results indicate that Safety ArtISt played four main roles in the safety lifecycle of AI-based systems for FPGAs. Firstly, it provided guidance in identifying the safety-critical role of activities such as sensitivity analyses for numeric representation and FPGA dimensioning to achieve safety. Furthermore, it allowed building qualitative and quantitative safety arguments from analyses and physical experimentation with actual FPGAs. It also allowed the early detection of safety issues—thus reducing project costs—and, ultimately, it uncovered relevant challenges not discussed in detail when designing safety-critical, explainable AI for FPGAs.

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