CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (TinyML) Acceleration on FPGAs

Prakash, Shvetank; Callahan, Timothy J.; Bushagour, Joseph; Banbury, Colby; Green, Alan V.; Warden, Pete; Ansell, Tim; Reddi, Vijay Janapa

doi:10.1109/ispass57527.2023.00024

Cited by 16 publications

(3 citation statements)

References 14 publications

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“…Here, the term playground refers to services that allow users to interact and play with software without prior complex setup or configuration (25). Moreover, these playgrounds enable users to iteratively (i.e., trial-and-error) develop and priorly test their entire implementation or specific modules (25,26). Because playgrounds have successfully enabled testing approaches in other settings, our work pursues a similar approach (25)(26)(27)(28).…”

Section: Methodsmentioning

confidence: 99%

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Will it run?—A proof of concept for smoke testing decentralized data analytics experiments

Welten,

Weber,

Holt

et al. 2024

Front. Med.

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The growing interest in data-driven medicine, in conjunction with the formation of initiatives such as the European Health Data Space (EHDS) has demonstrated the need for methodologies that are capable of facilitating privacy-preserving data analysis. Distributed Analytics (DA) as an enabler for privacy-preserving analysis across multiple data sources has shown its potential to support data-intensive research. However, the application of DA creates new challenges stemming from its distributed nature, such as identifying single points of failure (SPOFs) in DA tasks before their actual execution. Failing to detect such SPOFs can, for example, result in improper termination of the DA code, necessitating additional efforts from multiple stakeholders to resolve the malfunctions. Moreover, these malfunctions disrupt the seamless conduct of DA and entail several crucial consequences, including technical obstacles to resolve the issues, potential delays in research outcomes, and increased costs. In this study, we address this challenge by introducing a concept based on a method called Smoke Testing, an initial and foundational test run to ensure the operability of the analysis code. We review existing DA platforms and systematically extract six specific Smoke Testing criteria for DA applications. With these criteria in mind, we create an interactive environment called Development Environment for AuTomated and Holistic Smoke Testing of Analysis-Runs (DEATHSTAR), which allows researchers to perform Smoke Tests on their DA experiments. We conduct a user-study with 29 participants to assess our environment and additionally apply it to three real use cases. The results of our evaluation validate its effectiveness, revealing that 96.6% of the analyses created and (Smoke) tested by participants using our approach successfully terminated without any errors. Thus, by incorporating Smoke Testing as a fundamental method, our approach helps identify potential malfunctions early in the development process, ensuring smoother data-driven research within the scope of DA. Through its flexibility and adaptability to diverse real use cases, our solution enables more robust and efficient development of DA experiments, which contributes to their reliability.

show abstract

Section: Methodsmentioning

confidence: 99%

“…Moreover, these playgrounds enable users to iteratively (i.e., trial-and-error) develop and priorly test their entire implementation or specific modules (25,26). Because playgrounds have successfully enabled testing approaches in other settings, our work pursues a similar approach (25)(26)(27)(28).…”

Section: Methodsmentioning

confidence: 99%

Will it run?—A proof of concept for smoke testing decentralized data analytics experiments

Welten,

Weber,

Holt

et al. 2024

Front. Med.

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show abstract

“…There is emerging work in designing such tools for non-learning-based robotics hardware accelerators [29], [37], [41], [45]. Outside of the robotics domain, there is also early work on full-stack open-source tools and generalizable design flows for the rapid deployment of TinyML accelerators [42].…”

Section: Generalizeable and Automatable Design Flowsmentioning

confidence: 99%

Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots

Neuman

Plancher

Duisterhof³

et al. 2022

2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS)

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Machine learning (ML) has become a pervasive tool across computing systems. An emerging application that stresstests the challenges of ML system design is tiny robot learning, the deployment of ML on resource-constrained low-cost autonomous robots. Tiny robot learning lies at the intersection of embedded systems, robotics, and ML, compounding the challenges of these domains. Tiny robot learning is subject to challenges from size, weight, area, and power (SWAP) constraints; sensor, actuator, and compute hardware limitations; end-to-end system tradeoffs; and a large diversity of possible deployment scenarios. Tiny robot learning requires ML models to be designed with these challenges in mind, providing a crucible that reveals the necessity of holistic ML system design and automated end-to-end design tools for agile development. This paper gives a brief survey of the tiny robot learning space, elaborates on key challenges, and proposes promising opportunities for future work in ML system design.

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Hardware–Software Co-design for Ultra-Resource-Constrained Embedded Machine Learning Inference: A Printed Electronics Use Case

Zervakis,

Tahoori,

Henkel

2023

Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing

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CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (TinyML) Acceleration on FPGAs

Cited by 16 publications

References 14 publications

Will it run?—A proof of concept for smoke testing decentralized data analytics experiments

Will it run?—A proof of concept for smoke testing decentralized data analytics experiments

Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots

Hardware–Software Co-design for Ultra-Resource-Constrained Embedded Machine Learning Inference: A Printed Electronics Use Case

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