Intel i7的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列懶人包和總整理

電腦體系結構：量化研究方法（英文版·原書第6版）

為了解決Intel i7的問題，作者（美）亨尼斯 這樣論述：

在過去20多年的時間裡，本書一直是計算機領域的教師、學生和體系結構設計人員的必讀之作。兩位作者Hennessy和Patterson於2017年榮獲圖靈獎，肯定了他們對計算機領域持久而重要的技術貢獻。隨著處理器和系統架構的最新發展，第6版進行了全面修訂。這一版採用RISC-V指令集體系結構，這是一個現代的RISC指令集，被設計為免費且可公開採用的標準。 書中還增加了一個關於領域特定體系結構的新章節，並更新了關於倉儲級計算的章節，其中介紹了穀歌最新的WSC。與本書之前版本的目標一樣，本書致力於揭開計算機體系結構的神秘面紗，關注那些令人興奮的技術創新，同時強調良好的工程設計。

Intel i7進入發燒排行的影片

結合目標偵測技術與異常活動辨識之 自動視訊監控系統框架設計

為了解決Intel i7的問題，作者王俊曄 這樣論述：

自動視訊監控系統過去以人力為主，是安全巡防人員實地監控區域安全，現在以網路為主，運用數位化網路串連監控設備進行遠端監控，受惠於機器學習的快速發展，自動化視訊監控系統已朝向以智慧為主演進，整合計算機視覺技術來進行異常活動偵測。本論文目的係提出自動視訊監控系統架構以Apache Spark Streaming串流平台為基礎，整合大資料處理與機器學習、深度學習技術以解決異常活動偵測的問題與解高負載問題，並具負載平衡以及即時偵測等效能。使用方法以基礎伺服器核心架構利用 Apache Kafka，連結三台Intel Core i7與1080Ti GPU電腦主機為分散式運算環境，做為伺服節點之間的通訊中

介軟體。使用UCF-Crime資料集作為測試與訓練，還設立了教異常活動的異常場景，異常指數是經過sigmoid所產生，數值範圍0~1，越高分代表越異常，發生異常活動的那瞬間異常值為0.6。實驗結果：網路延遲的實驗中發現兩個Broker可以稍微降低延遲，開啟越多的Topic會提高硬體的負擔，5個Topic的延遲時間相比1個Topic高達4倍左右。開啟多核心可以大幅提高效能，當Topic數量逐漸成長，效能提升幅度也越來越明顯，數據結果證明監控系統能達到低延遲、高吞吐的目標。本研究貢獻有二：1.自動化監控系統能夠做到即時目標偵測。2.運用Kafka降低自動化監控系統的延遲。

電腦組成與設計：硬體/軟體介面（原書第5版·RISC-V版·英文版）

為了解決Intel i7的問題，作者（美）戴維·A.帕特森 這樣論述：

本書是經典著作《計算機組成與設計》繼MIPS版、ARM版之後的最新版本，這一版專注於RISC-V，是Patterson和Hennessy的又一力作。RISC-V指令集作為開源架構，是專為雲計算、移動計算以及各類嵌入式系統等現代計算環境設計的架構。本書更加關注後PC時代發生的變革，通過實例、練習等詳細介紹最新計算模式，更新的內容還包括平板電腦、雲基礎設施以及ARM（行動計算裝置）和x86 (雲計算)體系結構。 C H A P T E R S 1 Computer Abstractions and Technology 2 1.1 Introduction 3 1.2 Eight Great

Ideas in Computer Architecture 11 1.3 Below Your Program 13 1.4 Under the Covers 16 1.5 Technologies for Building Processors and Memory 24 1.6 Performance 28 1.7 The Power Wall 40 1.8 The Sea Change: The Switch from Uniprocessors to Multiprocessors 43 1.9 Real Stuff: Benchma the Intel Core i7 46 1.

10 Fallacies and Pitfalls 49 1.11 Concluding Remarks 52 1.12 Historical Perspective and Further Reading 54 1.13 Exercises 54 2 Instructions: Language of the Computer 60 2.1 Introduction 62 2.2 Operations of the Computer Hardware 63 2.3 Operands of the Computer Hardware 67 2.4 Signed and Unsigned Nu

mbers 74 2.5 Representing Instructions in the Computer 81 2.6 Logical Operations 89 2.7 Instructions for M Decisions 92 2.8 Supporting Procedures in Computer Hardware 98 2.9 Communicating with People 108 2.10 RISC-V Addressing for Wide Immediates and Addresses 113 2.11 Parallelism and Instructions:

Synchronization 121 2.12 Translating and Starting a Program 124 2.13 A C Sort Example to Put it All Together 133 2.14 Arrays versus Pointers 141 2.15 Advanced Material: Compiling C and Interpreting Java 144 2.16 Real Stuff: MIPS Instructions 145 2.17 Real Stuff: x86 Instructions 146 2.18 Real Stuff:

The Rest of the RISC-V Instruction Set 155 2.19 Fallacies and Pitfalls 157 2.20 Concluding Remarks 159 2.21 Historical Perspective and Further Reading 162 2.22 Exercises 162 3 Arithmetic for Computers 172 3.1 Introduction 174 3.2 Addition and Subtraction 174 3.3 Multiplication 177 3.4 Division 183

3.5 Floating Point 191 3.6 Parallelism and Computer Arithmetic: Subword Parallelism 216 3.7 Real Stuff: Streaming SIMD Extensions and Advanced Vector Extensions in x86 217 3.8 Going Faster: Subword Parallelism and Matrix Multiply 218 3.9 Fallacies and Pitfalls 222 3.10 Concluding Remarks 225 3.11 H

istorical Perspective and Further Reading 227 3.12 Exercises 227 4 The Processor 234 4.1 Introduction 236 4.2 Logic Design Conventions 240 4.3 Building a Datapath 243 4.4 A Simple Implementation Scheme 251 4.5 An Overview of Pipelining 262 4.6 Pipelined Datapath and Control 276 4.7 Data Hazards: Fo

rwarding versus Stalling 294 4.8 Control Hazards 307 4.9 Exceptions 315 4.10 Parallelism via Instructions 321 4.11 Real Stuff: The ARM Cortex-A53 and Intel Core i7 Pipelines 334 4.12 Going Faster: Instruction-Level Parallelism and Matrix Multiply 342 4.13 Advanced Topic: An Introduction to Digital D

esign Using a Hardware Design Language to Describe and Model a Pipeline and More Pipelining Illustrations 345 4.14 Fallacies and Pitfalls 345 4.15 Concluding Remarks 346 4.16 Historical Perspective and Further Reading 347 4.17 Exercises 347 5 Large and Fast: Exploiting Memory Hierarchy 364 5.1 Intr

oduction 366 5.2 Memory Technologies 370 5.3 The Basics of Caches 375 5.4 Measuring and Improving Cache Performance 390 5.5 Dependable Memory Hierarchy 410 5.6 Virtual Machines 416 5.7 Virtual Memory 419 5.8 A Common Framework for Memory Hierarchy 443 5.9 Using a Finite-State Machine to Control a Si

mple Cache 449 5.10 Parallelism and Memory Hierarchy: Cache Coherence 454 5.11 Parallelism and Memory Hierarchy: Redundant Arrays of Inexpensive Disks 458 5.12 Advanced Material: Implementing Cache Controllers 459 5.13 Real Stuff: The ARM Cortex-A53 and Intel Core i7 Memory Hierarchies 459 5.14 Real

Stuff: The Rest of the RISC-V System and Special Instructions 464 5.15 Going Faster: Cache Blo and Matrix Multiply 465 5.16 Fallacies and Pitfalls 468 5.17 Concluding Remarks 472 5.18 Historical Perspective and Further Reading 473 5.19 Exercises 473 6 Parallel Processors from Client to Cloud 490 6

.1 Introduction 492 6.2 The Difficulty of Creating Parallel Processing Programs 494 6.3 SISD, MIMD, SIMD, SPMD, and Vector 499 6.4 Hardware Multithreading 506 6.5 Multicore and Other Shared Memory Multiprocessors 509 6.6 Introduction to Graphics Processing Units 514 6.7 Clusters, Warehouse Scale Com

puters, and Other Message-Passing Multiprocessors 521 6.8 Introduction to Multiprocessor Network Topologies 526 6.9 Communicating to the Outside World: Cluster Netwo 529 6.10 Multiprocessor Benchmarks and Performance Models 530 6.11 Real Stuff: Benchma and Rooflines of the Intel Core i7 960 and the

NVIDIA Tesla GPU 540 6.12 Going Faster: Multiple Processors and Matrix Multiply 545 6.13 Fallacies and Pitfalls 548 6.14 Concluding Remarks 550 6.15 Historical Perspective and Further Reading 553 6.16 Exercises 553 A P P E N D I X The most beautiful thing we can experience is the mysterious. It

is the source of all true art and science. Albert Einstein, What I Believe, 1930 About This Book We believe that learning in computer science and engineering should reflect the current state of the field, as well as introduce the principles that are shaping computing. We also feel that readers

in every specialty of computing need to appreciate the organizational paradigms that determine the capabilities, performance, energy, and, ultimately, the success of computer systems. Modern computer technology requires professionals of every computing specialty to understand both hardware and so

ftware. The interaction between hardware and software at a variety of levels also offers a framework for understanding the fundamentals of computing. Whether your primary interest is hardware or software, computer science or electrical engineering, the central ideas in computer organization and desi

gn are the same. Thus, our emphasis in this book is to show the relationship between hardware and software and to focus on the concepts that are the basis for current computers. The recent switch from uniprocessor to multicore microprocessors confirmed the soundness of this perspective, given sinc

e the first edition. While programmers could ignore the advice and rely on computer architects, compiler writers, and silicon engineers to make their programs run faster or be more energy-efficient without change, that era is over. For programs to run faster, they must become parallel. While the goa

l of many researchers is to make it possible for programmers to be unaware of the underlying parallel nature of the hardware they are programming, it will take many years to realize this vision. Our view is that for at least the next decade, most programmers are going to have to understand the hardw

are/software interface if they want programs to run efficiently on parallel computers. The audience for this book includes those with little experience in assembly language or logic design who need to understand basic computer organization as well as readers with backgrounds in assembly language a

nd/or logic design who want to learn how to design a computer or understand how a system works and why it performs as it does. About the Other Book Some readers may be familiar with Computer Architecture: A Quantitative Approach, popularly known as Hennessy and Patterson. (This book in turn is o

ften called Patterson and Hennessy.) Our motivation in writing the earlier book was to describe the principles of computer architecture using solid engineering fundamentals and quantitative cost/performance tradeoffs. We used an approach that combined examples and measurements, based on commercial s

ystems, to create realistic design experiences. Our goal was to demonstrate that computer architecture could be learned using quantitative methodologies instead of a descriptive approach. It was intended for the serious computing professional who wanted a detailed understanding of computers. A maj

ority of the readers for this book do not plan to become computer architects. The performance and energy efficiency of future software systems will be dramatically affected, however, by how well software designers understand the basic hardware techniques at work in a system. Thus, compiler writers,

operating system designers, database programmers, and most other software engineers need a firm grounding in the principles presented in this book. Similarly, hardware designers must understand clearly the effects of their work on software applications. Thus, we knew that this book had to be much

more than a subset of the material in Computer Architecture, and the material was extensively revised to match the different audience. We were so happy with the result that the subsequent editions of Computer Architecture were revised to remove most of the introductory material; hence, there is much

less overlap today than with the first editions of both books. Why RISC-V for This Edition? The choice of instruction set architecture is

應用於MASK R-CNN卷積神經網絡之高效能硬體設計

為了解決Intel i7的問題，作者鄧文鈺 這樣論述：

卷積神經網路(Convolutional Neural Network, CNN)自從2012年AlexNet模型出現後，開啟一個非常重大的突破，因而帶動後續CNN模型熱烈的研究。CNN的應用非常廣泛，包括影像辨識、偵測、自動駕駛等，但由於CNN計算複雜度非常龐大，尤其為卷積層(Convolutional Layer)占用最多計算資源，故用於加速運算的專用硬體實現，變得非常迫切需要，尤其是應用於即時系統。本論文提出一個應用於Mask R-CNN模型之高效能計算CNN加速器，此加速器包含卷積層運算、最大池化(Max Pooling)運算及激活函數(Activation Function)計算。

為提升運算效能，本論文提出零值填充(Zero Padding)融入卷積層運算中，因而完全省去零值填充操作時所需的時間與硬體，使得CNN加速器運算變得更有效率。再者，由於提出的設計具無間斷時脈進行卷積層運算，因此有利後續下一層CNN的運算。對於處理10241024影像，採用77卷積核，當使用Intel Core i7 3.6 GHz個人電腦未配置GPU時，卷積層計算需花費約56 ms；然而使用我們設計的TSMC 90nm製程晶片，由邏輯合成的結果顯示，在時脈133 MHz下執行僅花費約7.9 ms。與前述個人電腦計算相比，加速約7倍，如我們的硬體採64個平行度處理，可加速約448倍。本設計

的晶片面積約使用253.6 K個邏輯閘數，動態功耗約116 mW。