The earlier the stage where we perform low power design, the higher the dynamic power reduction we achieve. In this paper, we focus on reducing switching activity in high-level synthesis, especially, in the problem of functional module binding, bus binding or register binding. We propose an effective low power bus binding algorithm based on the table decomposition method, to reduce switching activity. The proposed algorithm is based on the decomposition of the original problem into sub-problems by exploiting the optimal substructure. As a result, it finds an optimal or close-to-optimal binding solution with less computation time. Experimental results show the proposed method obtains a solution 2.3-22.2% closer to optimal solution than one with a conventional heuristic method, 8.0-479.2 times faster than the optimal one (at a threshold value of 1.0E+9).
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Ji-Hyung KIM, Jun-Dong CHO, "Low Power Bus Binding Exploiting Optimal Substructure" in IEICE TRANSACTIONS on Fundamentals,
vol. E94-A, no. 1, pp. 332-341, January 2011, doi: 10.1587/transfun.E94.A.332.
Abstract: The earlier the stage where we perform low power design, the higher the dynamic power reduction we achieve. In this paper, we focus on reducing switching activity in high-level synthesis, especially, in the problem of functional module binding, bus binding or register binding. We propose an effective low power bus binding algorithm based on the table decomposition method, to reduce switching activity. The proposed algorithm is based on the decomposition of the original problem into sub-problems by exploiting the optimal substructure. As a result, it finds an optimal or close-to-optimal binding solution with less computation time. Experimental results show the proposed method obtains a solution 2.3-22.2% closer to optimal solution than one with a conventional heuristic method, 8.0-479.2 times faster than the optimal one (at a threshold value of 1.0E+9).
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E94.A.332/_p
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@ARTICLE{e94-a_1_332,
author={Ji-Hyung KIM, Jun-Dong CHO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Low Power Bus Binding Exploiting Optimal Substructure},
year={2011},
volume={E94-A},
number={1},
pages={332-341},
abstract={The earlier the stage where we perform low power design, the higher the dynamic power reduction we achieve. In this paper, we focus on reducing switching activity in high-level synthesis, especially, in the problem of functional module binding, bus binding or register binding. We propose an effective low power bus binding algorithm based on the table decomposition method, to reduce switching activity. The proposed algorithm is based on the decomposition of the original problem into sub-problems by exploiting the optimal substructure. As a result, it finds an optimal or close-to-optimal binding solution with less computation time. Experimental results show the proposed method obtains a solution 2.3-22.2% closer to optimal solution than one with a conventional heuristic method, 8.0-479.2 times faster than the optimal one (at a threshold value of 1.0E+9).},
keywords={},
doi={10.1587/transfun.E94.A.332},
ISSN={1745-1337},
month={January},}
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TY - JOUR
TI - Low Power Bus Binding Exploiting Optimal Substructure
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 332
EP - 341
AU - Ji-Hyung KIM
AU - Jun-Dong CHO
PY - 2011
DO - 10.1587/transfun.E94.A.332
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E94-A
IS - 1
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - January 2011
AB - The earlier the stage where we perform low power design, the higher the dynamic power reduction we achieve. In this paper, we focus on reducing switching activity in high-level synthesis, especially, in the problem of functional module binding, bus binding or register binding. We propose an effective low power bus binding algorithm based on the table decomposition method, to reduce switching activity. The proposed algorithm is based on the decomposition of the original problem into sub-problems by exploiting the optimal substructure. As a result, it finds an optimal or close-to-optimal binding solution with less computation time. Experimental results show the proposed method obtains a solution 2.3-22.2% closer to optimal solution than one with a conventional heuristic method, 8.0-479.2 times faster than the optimal one (at a threshold value of 1.0E+9).
ER -