Lincoln AI Computing Survey (LAICS) Update

Reuther, Albert; Michaleas, Peter; Jones, Michael; Gadepally, Vijay; Samsi, Siddharth; Kepner, Jeremy

doi:10.1109/hpec58863.2023.10363568

2023 IEEE High Performance Extreme Computing Conference (HPEC) 2023

DOI: 10.1109/hpec58863.2023.10363568

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Lincoln AI Computing Survey (LAICS) Update

Albert Reuther,

Peter Michaleas,

Michael Jones

et al.

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2024

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Cited by 2 publications

References 9 publications

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An All Digital PLL in 22-nm FD SOI for Hardware Accelerated Embedded Systems

Asgari,

Mohammadpanah,

Ghafar-Zadeh

et al. 2024

2024 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

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An All Digital PLL in 22-nm FD SOI for Hardware Accelerated Embedded Systems

Asgari,

Mohammadpanah,

Ghafar-Zadeh

et al. 2024

2024 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)

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Performance tradeoffs of general-purpose digital hardware and application-specific analog hardware

Natalino,

Li,

Ozolins

et al. 2024

Machine Learning in Photonics

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The field of artificial intelligence & machine learning (AI/ML) has experienced unprecedented growth over the last decade driven by computationally demanding applications. The computing power has been so far provided by general-purpose digital hardware such as central processing units (CPUs) and graphics processing units (GPUs). As the potential for continuous technological advancements in digital electronics is brought into question, research is focusing on alternative paradigms such as application-specific analog hardware. Both electronics and photonic analog hardware are being actively investigated with promising results showing advantages in terms of processing speed and/or energy efficiency. However, a systematic comparison of these different hardware platforms in terms of high-level computing performance is missing. In this work, we compare these hardware platforms focusing on use cases with different requirements in terms of, e.g., compute capacity, efficiency, and density. The comparison highlights current advantages and key challenges to be addressed in each field.

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Lincoln AI Computing Survey (LAICS) Update

Cited by 2 publications

References 9 publications

An All Digital PLL in 22-nm FD SOI for Hardware Accelerated Embedded Systems

An All Digital PLL in 22-nm FD SOI for Hardware Accelerated Embedded Systems

Performance tradeoffs of general-purpose digital hardware and application-specific analog hardware

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