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Fine-Grained Clustering-Based Power Identification for Multicores

Mohamed R. Elshamy, Mehdi Elahi, Ahmad Patooghy, Abdel-Hameed A. Badawy

TL;DR

An innovative approach for NMF initializing to identify centroid data points of active cores as initial values is introduced, i.e., density-oriented spatial clustering to identify centroid data points of active cores as initial values, which enhances BPI accuracy by focusing on dense regions in the dataset and excluding outlier data points.

Abstract

Fine-grained power estimation in multicore Systems on Chips (SoCs) is crucial for efficient thermal management. BPI (Blind Power Identification) is a recent approach that determines the power consumption of different cores and the thermal model of the chip using only thermal sensor measurements and total power consumption. BPI relies on steady-state thermal data along with a naive initialization in its Non-negative Matrix Factorization (NMF) process, which negatively impacts the power estimation accuracy of BPI. This paper proposes a two-fold approach to reduce these impacts on BPI. First, this paper introduces an innovative approach for NMF initializing, i.e., density-oriented spatial clustering to identify centroid data points of active cores as initial values. This enhances BPI accuracy by focusing on dense regions in the dataset and excluding outlier data points. Second, it proposes the utilization of steady-state temperature data points to enhance the power estimation accuracy by leveraging the physical relationship between temperature and power consumption. Our extensive simulations of real-world cases demonstrate that our approach enhances BPI accuracy in estimating the power per core with no performance cost. For instance, in a four-core processor, the proposed approach reduces the error rate by 76% compared to BPI and by 24% compared to the state of the art in the literature, namely, Blind Power Identification Steady State (BPISS). The results underline the potential of integrating advanced clustering techniques in thermal model identification, paving the way for more accurate and reliable thermal management in multicores and SoCs.

Fine-Grained Clustering-Based Power Identification for Multicores

TL;DR

An innovative approach for NMF initializing to identify centroid data points of active cores as initial values is introduced, i.e., density-oriented spatial clustering to identify centroid data points of active cores as initial values, which enhances BPI accuracy by focusing on dense regions in the dataset and excluding outlier data points.

Abstract

Fine-grained power estimation in multicore Systems on Chips (SoCs) is crucial for efficient thermal management. BPI (Blind Power Identification) is a recent approach that determines the power consumption of different cores and the thermal model of the chip using only thermal sensor measurements and total power consumption. BPI relies on steady-state thermal data along with a naive initialization in its Non-negative Matrix Factorization (NMF) process, which negatively impacts the power estimation accuracy of BPI. This paper proposes a two-fold approach to reduce these impacts on BPI. First, this paper introduces an innovative approach for NMF initializing, i.e., density-oriented spatial clustering to identify centroid data points of active cores as initial values. This enhances BPI accuracy by focusing on dense regions in the dataset and excluding outlier data points. Second, it proposes the utilization of steady-state temperature data points to enhance the power estimation accuracy by leveraging the physical relationship between temperature and power consumption. Our extensive simulations of real-world cases demonstrate that our approach enhances BPI accuracy in estimating the power per core with no performance cost. For instance, in a four-core processor, the proposed approach reduces the error rate by 76% compared to BPI and by 24% compared to the state of the art in the literature, namely, Blind Power Identification Steady State (BPISS). The results underline the potential of integrating advanced clustering techniques in thermal model identification, paving the way for more accurate and reliable thermal management in multicores and SoCs.

Paper Structure

This paper contains 12 sections, 5 equations, 8 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Background
  4. Blind Power Identification Method
  5. The Proposed Approach
  6. BPI Sensitivity to Initialization and Outliers
  7. The Proposed Initialization Approach
  8. Experiments and Results
  9. Experimental setup
  10. HotSpot Simulation Results
  11. Comparison of Actual Power with Estimated Power
  12. Conclusions

Figures (8)

  • Figure 1: A thermal map for a $2 \times 2$ chip (in Kelvin), where the bottom right unit is activated.
  • Figure 2: DBSCAN Clustering Algorithm Illustration
  • Figure 3: K-distance graph to determine the value of $\epsilon$
  • Figure 4: The verification and testing flow of the proposed approach
  • Figure 5: The layout of the big.LITTLE+GPU SoC, utilized for testing the proposed approach b14
  • ...and 3 more figures