Automated HW/SW co-design for edge AI

Bringmann, Oliver; Ecker, Wolfgang; Feldner, Ingo; Frischknecht, Adrian; Gerum, Christoph; Hämäläinen, Timo D.; Hanif, Muhammad Abdullah; Klaiber, Michael J.; Mueller-Gritschneder, Daniel; Bernardo, Paul Palomero; Prebeck, Sebastian; Shafique, Muhammad

doi:10.1145/3478684.3479261

Cited by 12 publications

(3 citation statements)

References 49 publications

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“…The instructions supported by a particular processor and their byte-level encodings are defined as Instruction Set Architecture (ISA) [68]. The RISC-V expresses a fallback solution for untreated corner cases [69]. Investigating Open-Source Hardware (OSH) and contributing to designing hardware-based accelerators with ML represent enduring concerns within such a high-reliable embedded ecosystem [70].…”

Section: Hardware Accelerators With Fpga and Risc-vmentioning

confidence: 99%

Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

Wang

et al. 2023

Drones

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In the Machine Learning (ML) era, faced with challenges, including exponential multi-sensor data, an increasing number of actuators, and data-intensive algorithms, the development of Unmanned Aerial Vehicles (UAVs) is standing on a new footing. In particular, the Flight Management System (FMS) plays an essential role in UAV design. However, the trade-offs between performance and SWaP-C (Size, Weight, Power, and Cost) and reliability–efficiency are challenging to determine for such a complex system. To address these issues, the identification of a successful approach to managing heterogeneity emerges as the critical question to be answered. This paper investigates Heterogeneous Computing (HC) integration in FMS in the UAV domain from academia to industry. The overview of cross-layer FMS design is firstly described from top–down in the abstraction layer to left–right in the figurative layer. In addition, the HC advantages from Light-ML, accelerated Federated Learning (FL), and hardware accelerators are highlighted. Accordingly, three distinct research focuses detailed with visual-guided landing, intelligent Fault Diagnosis and Detection (FDD), and controller-embeddable Power Electronics (PE) to distinctly illustrate advancements of the next-generation FMS design from sensing, and computing, to driving. Finally, recommendations for future research and opportunities are discussed. In summary, this article draws a road map that considers the heterogeneous advantages to conducting the Flight-Management-as-a-Service (FMaaS) platform for UAVs.

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Section: Hardware Accelerators With Fpga and Risc-vmentioning

confidence: 99%

Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

Wang

et al. 2023

Drones

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“…The novel workflow is named co-design because ML experts and hardware engineers are involved together from the start in the solution design and actively exchange operational knowledge [23]. Hardware engineers approach the mathematical notions underlying the ML algorithms and propose suitable hardware components for efficient translations.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Oriented Survey on Tiny Machine Learning

Capogrosso,

Cunico,

Cheng

et al. 2024

IEEE Access

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The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware devices and their learning-based software architectures. TinyML carries an essential role within the fourth and fifth industrial revolutions in helping societies, economies, and individuals employ effective AI-infused computing technologies (e.g., smart cities, automotive, and medical robotics). Given its multidisciplinary nature, the field of TinyML has been approached from many different angles: this comprehensive survey wishes to provide an up-to-date overview focused on all the learning algorithms within TinyML-based solutions. The survey is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological flow, allowing for a systematic and complete literature survey. In particular, firstly, we will examine the three different workflows for implementing a TinyML-based system, i.e., MLoriented, HW-oriented, and co-design. Secondly, we propose a taxonomy that covers the learning panorama under the TinyML lens, examining in detail the different families of model optimization and design, as well as the state-of-the-art learning techniques. Thirdly, this survey will present the distinct features of hardware devices and software tools that represent the current state-of-the-art for TinyML intelligent edge applications. Finally, we discuss the challenges and future directions.

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“…Such an AI transformation in the ModEx loop is needed to efficiently and accurately integrate the DOE's observational capabilities and platforms, 1 process models and software infrastructure, 2 and computational hardware. 3 However, achieving this AI-enabled ModEx vision requires significant advancements in codesign and associated AI architectures (Germann et al 2013;Zhang et al 2019;Beckman et al 2020;Descour et al 2021;Bringmann et al 2021). Codesign as described by Pao (2011), Barrett et al (2013), andGermann (2021) refers to a computer system design process where scientific problem requirements influence architecture design, technology, and constraints inform the formulation and design of algorithms and software.…”

Section: Introductionmentioning

confidence: 99%

Perspectives on AI Architectures and Codesign for Earth System Predictability

Mudunuru,

Ang,

Halappanavar

et al. 2024

Artificial Intelligence for the Earth Systems

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Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, lab, modeling, and analysis activities, called ModEx. The BER’s ‘Model-Experimentation’, ModEx, is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process-based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the ‘AI Architectures and Co-design’ session and associated outcomes. The AI Architectures and Co-design session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including: (1) DOE high-performance computing (HPC) Systems, (2) Cloud HPC Systems, and (3) Edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this co-design area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as: (1) reimagining co-design, (2) data acquisition to distribution, (3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with earth system modeling and simulation, and (4) AI-enabled sensor integration into earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper.

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Automated HW/SW co-design for edge AI

Cited by 12 publications

References 49 publications

Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

A Machine Learning-Oriented Survey on Tiny Machine Learning

Perspectives on AI Architectures and Codesign for Earth System Predictability

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