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书目名称Minimizing and Exploiting Leakage in VLSI Design影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0634623<br><br> <br><br>书目名称Minimizing and Exploiting Leakage in VLSI Design影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0634623<br><br> <br><br>书目名称Minimizing and Exploiting Leakage in VLSI Design网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0634623<br><br> <br><br>书目名称Minimizing and Exploiting Leakage in VLSI Design网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0634623<br><br> <br><br>书目名称Minimizing and Exploiting Leakage in VLSI Design被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0634623<br><br> <br><br>书目名称Minimizing and Exploiting Leakage in VLSI Design被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0634623<br><br> <br><br>书目名称Minimizing and Exploiting Leakage in VLSI Design年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0634623<br><br> <br><br>书目名称Minimizing and Exploiting Leakage in VLSI Design年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0634623<br><br> <br><br>书目名称Minimizing and Exploiting Leakage in VLSI Design读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0634623<br><br> <br><br>书目名称Minimizing and Exploiting Leakage in VLSI Design读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0634623<br><br> <br><br>
调情
发表于 2025-3-21 20:42:12
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删减
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漫不经心
发表于 2025-3-22 07:56:45
https://doi.org/10.1007/978-1-4419-0950-3ASIC; EDA; Electronic Design Automation; Leakage; Low Power Design; Sub-threshold logic; Transistor; VLSI; V
Prophylaxis
发表于 2025-3-22 10:34:05
978-1-4899-8529-3Springer-Verlag US 2010
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Unsaturated-Fat
发表于 2025-3-22 23:49:06
e second technique reduces leakage by applying the optimal Reverse Body Bias (RBB) voltage. This book also shows readers how to turn the leakage problem into an opportunity, through the use of sub-threshold logic..978-1-4899-8529-3978-1-4419-0950-3
率直
发表于 2025-3-23 01:49:25
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斜谷
发表于 2025-3-23 07:32:12
Self-Competitive Neural NetworksNN tries to improve its accuracy by competing with itself (generating hard samples and then learning them), the technique is called Self-Competitive Neural Network (SCNN). To generate such samples, we pose the problem as an optimization task, where the network weights are fixed and use a gradient de