A Scalable, Heterogeneous Hardware Platform for Accelerated AIoT based on Microservers

• 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.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)

Nyckelord

performance classification.

deep learning

FPGA

microserver

accelerator

energy-efficiency

IoT

(far) edge-computing

machine learning

AIoT

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