The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Suhu tinggi memberi kesan buruk pada kebolehpercayaan, prestasi dan kuasa kebocoran litar. Semasa sintesis tingkah laku, kedua-dua peruntukan penggunaan sumber dan pengikatan sumber mempengaruhi profil terma. Sintesis tingkah laku sedar terma semasa tidak menggunakan maklumat lokasi sumber daripada pelan lantai dan di samping itu hanya menumpukan pada mengikat, mengabaikan peruntukan. Kertas kerja ini mencadangkan sintesis tingkah laku sedar haba dengan peruntukan penggunaan sumber. Berdasarkan metrik hibrid maklumat lokasi fizikal dan suhu, kami mengikat semula operasi dan memperuntukkan semula bilangan sumber di bawah kekangan kawasan. Pendekatan kami mengawal suhu puncak dengan berkesan dan mewujudkan ketumpatan kuasa sekata antara sumber pelbagai jenis dan dalam sumber jenis yang sama. Keputusan eksperimen menunjukkan purata 8.6
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Salinan
Junbo YU, Qiang ZHOU, Gang QU, Jinian BIAN, "Peak Temperature Reduction by Physical Information Driven Behavioral Synthesis with Resource Usage Allocation" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 12, pp. 3151-3159, December 2009, doi: 10.1587/transfun.E92.A.3151.
Abstract: High temperature adversely impacts on circuit's reliability, performance, and leakage power. During behavioral synthesis, both resource usage allocation and resource binding influence thermal profile. Current thermal-aware behavioral syntheses do not utilize location information of resources from floorplan and in addition only focus on binding, ignoring allocation. This paper proposes thermal-aware behavioral synthesis with resource usage allocation. Based on a hybrid metric of physical location information and temperature, we rebind operations and reallocate the number of resources under area constraint. Our approach effectively controls peak temperature and creates even power densities among resources of different types and within resources of the same type. Experimental results show an average of 8.6
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.3151/_p
Salinan
@ARTICLE{e92-a_12_3151,
author={Junbo YU, Qiang ZHOU, Gang QU, Jinian BIAN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Peak Temperature Reduction by Physical Information Driven Behavioral Synthesis with Resource Usage Allocation},
year={2009},
volume={E92-A},
number={12},
pages={3151-3159},
abstract={High temperature adversely impacts on circuit's reliability, performance, and leakage power. During behavioral synthesis, both resource usage allocation and resource binding influence thermal profile. Current thermal-aware behavioral syntheses do not utilize location information of resources from floorplan and in addition only focus on binding, ignoring allocation. This paper proposes thermal-aware behavioral synthesis with resource usage allocation. Based on a hybrid metric of physical location information and temperature, we rebind operations and reallocate the number of resources under area constraint. Our approach effectively controls peak temperature and creates even power densities among resources of different types and within resources of the same type. Experimental results show an average of 8.6
keywords={},
doi={10.1587/transfun.E92.A.3151},
ISSN={1745-1337},
month={December},}
Salinan
TY - JOUR
TI - Peak Temperature Reduction by Physical Information Driven Behavioral Synthesis with Resource Usage Allocation
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 3151
EP - 3159
AU - Junbo YU
AU - Qiang ZHOU
AU - Gang QU
AU - Jinian BIAN
PY - 2009
DO - 10.1587/transfun.E92.A.3151
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2009
AB - High temperature adversely impacts on circuit's reliability, performance, and leakage power. During behavioral synthesis, both resource usage allocation and resource binding influence thermal profile. Current thermal-aware behavioral syntheses do not utilize location information of resources from floorplan and in addition only focus on binding, ignoring allocation. This paper proposes thermal-aware behavioral synthesis with resource usage allocation. Based on a hybrid metric of physical location information and temperature, we rebind operations and reallocate the number of resources under area constraint. Our approach effectively controls peak temperature and creates even power densities among resources of different types and within resources of the same type. Experimental results show an average of 8.6
ER -