Abstract
As the computer architecture community moves toward the end of traditional device scaling, domain-specific architectures are becoming more pervasive. Given the number of diverse workloads and emerging heterogeneous architectures, exploration of this design space is a constrained optimization problem in a high-dimensional parameter space. In this respect, predicting workload performance both accurately and efficiently is a critical task for this exploration. In this paper, we present Deffe: a framework to estimate workload performance across varying architectural configurations. Deffe uses machine learning to improve the performance of this design space exploration. By casting the work of performance prediction itself as transfer learning tasks, the modelling component of Deffe can leverage the learned knowledge on one workload and "transfer" it to a new workload. Our extensive experimental results on a contemporary architecture toolchain (RISC-V and GEM5) and infrastructure show that the method can achieve superior testing accuracy with an effective reduction of 32-80× in terms of the amount of required training data. The overall run-time can be reduced from 400 hours to 5 hours when executed over 24 CPU cores. The infrastructure component of Deffe is based on scalable and easy-to-use open-source software components.
Original language | English |
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Title of host publication | 17th ACM International Conference on Computing Frontiers 2020, CF 2020 - Proceedings |
Publisher | Association for Computing Machinery, Inc |
Pages | 182-191 |
Number of pages | 10 |
ISBN (Electronic) | 9781450379564 |
DOIs | |
State | Published - May 11 2020 |
Event | 17th ACM International Conference on Computing Frontiers, CF 2020 - Catania, Italy Duration: May 11 2020 → May 13 2020 |
Publication series
Name | 17th ACM International Conference on Computing Frontiers 2020, CF 2020 - Proceedings |
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Conference
Conference | 17th ACM International Conference on Computing Frontiers, CF 2020 |
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Country/Territory | Italy |
City | Catania |
Period | 05/11/20 → 05/13/20 |
Funding
In this paper, we have presented Deffe, a general framework for workload performance prediction in domain-specific computing. The modeling component of the framework is built on a CNN and the transfer learning technique. The modeling component is supported by the infrastructure component, which is based on a
Keywords
- RISC-V
- machine learning
- multichannel convolution
- transfer learning
- workload characterization