Performance modelling of heterogeneous ISA multicore architectures

Boran, Nirmal Kumar; Meghwal, Rameshwar Prasad; Sharma, Kuldeep; Kumar, Binod; Singh, Virendra

doi:10.1109/ewdts.2016.7807641

Cited by 4 publications

(2 citation statements)

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“…Regression techniques are also used in heterogeneous systems for cross platform performance estimation. Examples include [199], [20], [133], and [10]. Zheng et al [199] used Lasso Linear Regression and Constrained Locally Sparse Linear Regression (CLSLR) to explore correlation among performance of same programs on different platforms to perform cross-platform performance prediction.…”

Section: Regression Techniques -Statistics Meets Machine Learningmentioning

confidence: 99%

“…LACross is shown to have an average error less than 2% in entire program's performance estimation, in contrast to more than 5% error in [198] for SD-VBS benchmark suite [176]. Boran et al [20] followed Pricopi et al's [142] work and used regression techniques to estimate execution cycle count of a particular ISA core based on the performance statistics of another ISA core. This model is used to dynamically schedule programs in a heterogeneous-ISA multi-core system.…”

Section: Regression Techniques -Statistics Meets Machine Learningmentioning

confidence: 99%

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The Tribes of Machine Learning and the Realm of Computer Architecture

Akram¹,

Lowe-Power²

2020

Preprint

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Machine learning techniques have influenced the field of computer architecture like many other fields. This paper studies how the fundamental machine learning techniques can be applied towards computer architecture problems. We also provide a detailed survey of computer architecture research that employs different machine learning methods. Finally, we present some future opportunities and the outstanding challenges that need to be overcome to exploit full potential of machine learning for computer architecture.

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