Integration verification across software and hardware for a simple embedded system

Erbsen, Andres; Gruetter, Samuel; Choi, Joonwon; Wood, Clark; Chlipala, Adam

doi:10.1145/3453483.3454065

Cited by 16 publications

(5 citation statements)

References 32 publications

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“…Finally, while our approach treats job segments as black boxes executed between their best-case and worst-case execution times, verifying the code itself is important. Approaches such as the ones in [31,30] can be thus complementary to ours. inevitable delay transitions is reached (at this new state, delay transitions are inevitable exactly for the same reasons they are inevitable in S above).…”

Section: Discussionmentioning

confidence: 95%

“…The two obvious problems of seqlocks w.r.t. predictability are that the reader side can be starved by successive back-to-back writes, and that the reader side can read stale data due to the ABA problem when the counter repeats after 2 31 write operations [29]. Though both are unlikely to happen in real systems, we need to eliminate them to abide by safety-critical standards.…”

Section: Bounding Seqlocksmentioning

confidence: 99%

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Compositional verification of embedded real-time systems

Foughali

Hladik

Zuepke

2023

Journal of Systems Architecture

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Section: Discussionmentioning

confidence: 95%

Section: Bounding Seqlocksmentioning

confidence: 99%

Compositional verification of embedded real-time systems

Foughali

Hladik

Zuepke

2023

Journal of Systems Architecture

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“…EBD is implemented including a microcontroller with several general-purpose input-output (GPIO) pins. This system or application is used in medical instrumentation, process control, automated vehicle control, and communication devices [19]- [21].…”

Section: Methodsmentioning

confidence: 99%

Intra-class deep learning object detection on embedded computer system

Santiary,

Swardika,

Dewi

et al. 2024

IJ-AI

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<span lang="EN-US">Implementation of artificial intelligence tends to be portable, mobile and embeds in embedded computer system (EBD). EBD is a special-purpose computer with limited capacity in a small-form size. Deep learning (DL) had known as cutting edges for object recognition. With DL, object feature extraction analysis is omitted. DL requires large computing resources and capacity. Implement DL algorithm on EBD goal to achieves high detection accuracy and high-efficiency resources. Hence, be able to cope with intra-class variations, and image disturbances. By those challenges and limitations, this study reports the performance of EBD to recognize an object which has high variations in their class, through an optimal raw-input dataset. The raw-input dataset performed optimization process with a supervisor. Yield is the proper optimal input dataset in size. The performance results observed begin from training dataset until evaluation stage of DL. The comparison performs in efficiency resources, loss, validation-loss, timesteps, and detection accuracy by multiclass confusion matrix analysis. This study shows through this purpose method efficient resources are highly archived. Shorter timesteps ensure training stage is successful, and detection accuracy is perfectly archived. In addition, this study proves DL method archived great performances in classifying object that has identical structure.</span>

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“…The embedded system is an information processing system embedded into a particular product [13]. This technology is becoming popular because it has several advantages [14,15]. Computing components in this system have many kinds of microcontrollers that can be used according to specification requirements, such as ATmega ICs, Arduino ICs, Intel ICs, and so on [16].…”

Section: A Embedded Systemmentioning

confidence: 99%

Embedded System Training Kit for Artificial Intelligence

Gusti

2024

IJIET

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Ever-developing science and technology require us always to be ready to adapt. The current challenging era is Society 5.0, which places a strong emphasis on harnessing human potential to overcome diverse challenges, including the development of Artificial Intelligence (AI) technology. Therefore, to improve the quality of human resources, this paper proposes the development of an artificial intelligence training kit based on embedded systems according to industry needs. The development of a training kit utilizing the RnD method was accomplished through the use of the ADDIE (analysis design, development, implementation, and evaluation) model. This model encompasses analysis, design, development, implementation, and evaluation. The technology of the training kit combines fuzzy logic, Artificial Neural Network (ANN), and image processing, consisting of hardware, software, and job sheets. The controller used to process embedded systems is the ESP32 board. Arduino UNO is used to execute the training results of the artificial intelligence system. The training kit performance test results show that all AI programs run optimally, and each component can function according to performance indicators. A group of subject matter and media experts evaluated the feasibility of the project and determined it to be very feasible, with a score of 83.64% and 86.67%. In addition, a feasibility test was conducted with 38 respondents, resulting in a score of 83.35%, and it was categorized as a very feasible tool. The effectiveness of the training kit applied to the experimental class resulted in a post-test mean score of 89.58, while the control class mean score was 76.39, so the AI training kit showed more effectiveness.

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Integration verification across software and hardware for a simple embedded system

Cited by 16 publications

References 32 publications

Compositional verification of embedded real-time systems

Compositional verification of embedded real-time systems

Intra-class deep learning object detection on embedded computer system

Embedded System Training Kit for Artificial Intelligence

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