語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
FindBook
Google Book
Amazon
博客來
Explainable Artificial Intelligence for Better Design of Very Large Scale Integrated Circuits.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Explainable Artificial Intelligence for Better Design of Very Large Scale Integrated Circuits./
作者:
Zeng, Wei.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:
106 p.
附註:
Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Contained By:
Dissertations Abstracts International83-03B.
標題:
Electrical engineering. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28719980
ISBN:
9798538121359
Explainable Artificial Intelligence for Better Design of Very Large Scale Integrated Circuits.
Zeng, Wei.
Explainable Artificial Intelligence for Better Design of Very Large Scale Integrated Circuits.
- Ann Arbor : ProQuest Dissertations & Theses, 2021 - 106 p.
Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
Thesis (Ph.D.)--The University of Wisconsin - Madison, 2021.
This item must not be sold to any third party vendors.
With the advance of Very Large Scale Integration (VLSI) technology, the design process of VLSI circuits becomes more complex, challenging, and time-consuming. Recent years have seen a rising trend of machine learning (ML) incorporated in VLSI design flow for better and more efficient design and implementation of integrated circuits.Explainable Artificial Intelligence (XAI) is an emerging technique that aims to perform prediction tasks while providing explanations for the predictions. XAI adds transparency and trustworthiness to ML models, leading to better human understanding and exploitation of the models. With ML being applied in VLSI design, it is desirable to adopt ideas from XAI for even better and more trustworthy outcomes of VLSI design.This dissertation explores the usage of Shapley Additive Explanation (SHAP)--a recent development in XAI, on different aspects and stages of VLSI design flow. Specifically, we propose three techniques that adopt SHAP in front-end and back-end design flows, including (a) SHAP-guided layout obfuscation for enhanced hardware security in split manufacturing, (b) explainable routability prediction, which accelerates the physical design flow and provides hints for improving the design, and (c) explainable-ML-guided approximate logic synthesis for area-efficient computing in error-tolerant applications. These are the first works that incorporate XAI into VLSI design methodology. All of them achieve better results than their conventional counterparts or existing works in similar settings.
ISBN: 9798538121359Subjects--Topical Terms:
649834
Electrical engineering.
Subjects--Index Terms:
Computer-aided design
Explainable Artificial Intelligence for Better Design of Very Large Scale Integrated Circuits.
LDR
:02810nmm a2200385 4500
001
2348653
005
20220912135627.5
008
241004s2021 ||||||||||||||||| ||eng d
020
$a
9798538121359
035
$a
(MiAaPQ)AAI28719980
035
$a
AAI28719980
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
Zeng, Wei.
$3
3688023
245
1 0
$a
Explainable Artificial Intelligence for Better Design of Very Large Scale Integrated Circuits.
260
1
$a
Ann Arbor :
$b
ProQuest Dissertations & Theses,
$c
2021
300
$a
106 p.
500
$a
Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
500
$a
Advisor: Davoodi, Azadeh.
502
$a
Thesis (Ph.D.)--The University of Wisconsin - Madison, 2021.
506
$a
This item must not be sold to any third party vendors.
520
$a
With the advance of Very Large Scale Integration (VLSI) technology, the design process of VLSI circuits becomes more complex, challenging, and time-consuming. Recent years have seen a rising trend of machine learning (ML) incorporated in VLSI design flow for better and more efficient design and implementation of integrated circuits.Explainable Artificial Intelligence (XAI) is an emerging technique that aims to perform prediction tasks while providing explanations for the predictions. XAI adds transparency and trustworthiness to ML models, leading to better human understanding and exploitation of the models. With ML being applied in VLSI design, it is desirable to adopt ideas from XAI for even better and more trustworthy outcomes of VLSI design.This dissertation explores the usage of Shapley Additive Explanation (SHAP)--a recent development in XAI, on different aspects and stages of VLSI design flow. Specifically, we propose three techniques that adopt SHAP in front-end and back-end design flows, including (a) SHAP-guided layout obfuscation for enhanced hardware security in split manufacturing, (b) explainable routability prediction, which accelerates the physical design flow and provides hints for improving the design, and (c) explainable-ML-guided approximate logic synthesis for area-efficient computing in error-tolerant applications. These are the first works that incorporate XAI into VLSI design methodology. All of them achieve better results than their conventional counterparts or existing works in similar settings.
590
$a
School code: 0262.
650
4
$a
Electrical engineering.
$3
649834
650
4
$a
Computer engineering.
$3
621879
650
4
$a
Intellectual property.
$3
572975
650
4
$a
Artificial intelligence.
$3
516317
653
$a
Computer-aided design
653
$a
Electronic design automation
653
$a
Explainable artificial intelligence
653
$a
Machine learning
653
$a
Very large scale integration
690
$a
0544
690
$a
0464
690
$a
0800
690
$a
0513
710
2
$a
The University of Wisconsin - Madison.
$b
Electrical Engineering.
$3
2095968
773
0
$t
Dissertations Abstracts International
$g
83-03B.
790
$a
0262
791
$a
Ph.D.
792
$a
2021
793
$a
English
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28719980
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9471091
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入
(1)帳號:一般為「身分證號」;外籍生或交換生則為「學號」。 (2)密碼:預設為帳號末四碼。
帳號
.
密碼
.
請在此電腦上記得個人資料
取消
忘記密碼? (請注意!您必須已在系統登記E-mail信箱方能使用。)