| 1. |
- Alvarez, Lluc, et al.
(författare)
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eProcessor: European, Extendable, Energy-Efficient, Extreme-Scale, Extensible, Processor Ecosystem
- 2023
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Ingår i: Proceedings of the 20th ACM International Conference on Computing Frontiers 2023, CF 2023. ; , s. 309-314
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Konferensbidrag (refereegranskat)abstract
- The eProcessor project aims at creating a RISC-V full stack ecosystem. The eProcessor architecture combines a high-performance out-of-order core with energy-efficient accelerators for vector processing and artificial intelligence with reduced-precision functional units. The design of this architecture follows a hardware/software co-design approach with relevant application use cases from the high-performance computing, bioinformatics and artificial intelligence domains. Two eProcessor prototypes will be developed based on two fabricated eProcessor ASICs integrated into a computer-on-module.
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| 2. |
- Cristal, Adrian, et al.
(författare)
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LEGaTO: First Steps Towards Energy-Efficient Toolset for Heterogeneous Computing
- 2018
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Ingår i: ACM International Conference Proceeding Series. - New York, NY, USA : ACM. ; , s. 210-217
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Konferensbidrag (refereegranskat)abstract
- LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.
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| 3. |
- Cristal, Adrian, et al.
(författare)
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LEGaTO: Towards Energy-Efficient, Secure, Fault-tolerant Toolset for Heterogeneous Computing
- 2018
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Ingår i: Proceedings of the 15th ACM International Conference on Computing Frontiers. - New York, NY, USA : ACM. - 9781450357616 ; , s. 276-278
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Konferensbidrag (refereegranskat)abstract
- LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.
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| 4. |
- Griessl, René, et al.
(författare)
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A Scalable, Heterogeneous Hardware Platform for Accelerated AIoT based on Microservers
- 2023
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Ingår i: Shaping the Future of IoT with Edge Intelligence How Edge Computing Enables the Next Generation of IoT Applications. - 9788770040273 ; , s. 179-196
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Performance and energy efficiency are key aspects of next-generation AIoT hardware. This chapter presents a scalable, heterogeneous hardware platform for accelerated AIoT based on microserver technology. It integrates several accelerator platforms based on technologies like CPUs, embedded GPUs, FPGAs, or specialized ASICs, supporting the full range of the cloud−edgeIoT continuum. The modular microserver approach enables the integrationof different, heterogeneous accelerators into one platform. Benchmarking the various accelerators takes performance, energy efficiency, and accuracy into account. The results provide a solid overview of available accelerator solutions and guide hardware selection for AIoT applications from the far edge to the cloud.
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| 5. |
- Griessl, R., et al.
(författare)
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Evaluation of heterogeneous AIoT Accelerators within VEDLIoT
- 2023
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Ingår i: Proceedings -Design, Automation and Test in Europe, DATE. - 1530-1591. ; 2023-April
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Konferensbidrag (refereegranskat)abstract
- Within VEDLIoT, a project targeting the development of energy-efficient Deep Learning for distributed AIoT applications, several accelerator platforms based on technologies like CPUs, embedded GPUs, FPGAs, or specialized ASICs are evaluated. The VEDLIoT approach is based on modular and scalable cognitive IoT hardware platforms. Modular microserver technology enables the integration of different, heterogeneous accelerators into one platform. Benchmarking of the different accelerators takes into account performance, energy efficiency and accuracy. The results in this paper provide a solid overview regarding available accelerator solutions and provide guidance for hardware selection for AIoT applications from far edge to cloud. VEDLIoT is an H2020 EU project which started in November 2020. It is currently in an intermediate stage. The focus is on the considerations of the performance and energy efficiency of hardware accelerators. Apart from the hardware and accelerator focus presented in this paper, the project also covers toolchain, security and safety aspects. The resulting technology is tested on a wide range of AIoT applications.
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| 6. |
- Mika, Kevin, et al.
(författare)
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VEDLIoT: Next generation accelerated AIoT systems and applications
- 2023
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Ingår i: Proceedings of the 20th ACM International Conference on Computing Frontiers 2023, CF 2023. - 9798400701405
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Konferensbidrag (refereegranskat)abstract
- The VEDLIoT project aims to develop energy-efficient Deep Learning methodologies for distributed Artificial Intelligence of Things (AIoT) applications. During our project, we propose a holistic approach that focuses on optimizing algorithms while addressing safety and security challenges inherent to AIoT systems. The foundation of this approach lies in a modular and scalable cognitive IoT hardware platform, which leverages microserver technology to enable users to configure the hardware to meet the requirements of a diverse array of applications. Heterogeneous computing is used to boost performance and energy efficiency. In addition, the full spectrum of hardware accelerators is integrated, providing specialized ASICs as well as FPGAs for reconfigurable computing. The project's contributions span across trusted computing, remote attestation, and secure execution environments, with the ultimate goal of facilitating the design and deployment of robust and efficient AIoT systems. The overall architecture is validated on use-cases ranging from Smart Home to Automotive and Industrial IoT appliances. Ten additional use cases are integrated via an open call, broadening the range of application areas.
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