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Abstract
Fast evaluation is important for processor design space exploration in order to increase the probability of encountering the optimal design in the vast space of configurations. Previous work has focused on estimation of dynamic multi-issue processors, which does not consider the effects of a varying instruction-set on the estimation through heuristic compilation. This paper presents a cycle count estimation method for application-specific static multi-issue processors with customizable datapaths via machine learning techniques that can estimate cycle counts for any architecture configuration after the initial profiling of the program. Among the estimated models, the residual neural network model achieves the lowest mean relative error of 4.7% while being orders of magnitude faster than running the recompilation and simulation steps.
Original language | English |
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Title of host publication | 2024 IEEE Nordic Circuits and Systems Conference, NORCAS 2024 - Proceedings |
Editors | Jari Nurmi, Joachim Rodrigues, Luca Pezzarossa, Viktor Aberg, Baktash Behmanesh |
Publisher | IEEE |
ISBN (Electronic) | 979-8-3315-1766-3 |
DOIs | |
Publication status | Published - 2024 |
MoE publication type | A4 Conference publication |
Event | IEEE Nordic Circuits and Systems Conference - Lund, Sweden Duration: 29 Oct 2024 → 30 Oct 2024 |
Publication series
Name | 2024 IEEE Nordic Circuits and Systems Conference, NORCAS 2024 - Proceedings |
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Conference
Conference | IEEE Nordic Circuits and Systems Conference |
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Abbreviated title | NORCAS |
Country/Territory | Sweden |
City | Lund |
Period | 29/10/2024 → 30/10/2024 |
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Dive into the research topics of 'Cycle Count Estimation of VLIW Processors Using Machine Learning'. Together they form a unique fingerprint.Projects
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ADEREHA Pajarinen: Tekoälyyn perustuva energia- ja resurssitehokkaiden laitteistokiihdyttimien suunnittelu / Konsortio: ADEREHA
Pajarinen, J. (Principal investigator), Wang, H. (Project Member), Yang, W. (Project Member), Shrestha, J. (Project Member) & Zhang, Y. (Project Member)
01/01/2023 → 31/12/2025
Project: Academy of Finland: Other research funding