人工智能芯片技术白皮书2018(中文版)
2019年共需科目：人工智能与健康考试(20)
33.对人工智能发展态势的判断中的新挑战是指人工智能发展的（）带来新挑战。
D.不确定性
34.人工智能的行业应用逐步展开，在（）、医疗、安防等领域率先突破。
A.金融
35.旷视科技成立于2011年底，在（）领域达到世界领先水平，先后完成四轮融资共计6000万美元，现估值2亿美元。
B搜索数据、百万级（）、百万级定位数据。
A.图像视频数据
24.英国政府从四方面提出了促进英国AI产业发展的重要行动建议：数据获取、（）、研究转化和行业发展。
D.人才培养
25.日本政府的人工智能产业化路线图第一阶段确立无人工厂、（）技术。
C.无人农场
B.国防
34.云知声，成立于2012年6月，是一家专注物联网人工智能服务，拥有（）、世界顶尖智能语音识别技术的企业。
C.完全自主知识产权
35.我国新一代人工智能发展的总体部署中强化四大支撑是指全面支撑科技、经济、（）和国家安全。
D.社会发展
36.我国新一代人工智能发展的重点任务四：加强人工智能领域（）。
A.创新综合监管机制建设
B.夯实人工智能应用的数据基础
C.利用人工智能推动卫生信息化建设
D.加大政策扶持力度，制定人工智能在医疗领域的发展规划
46.医共体发生在（）之间。
B.专科医院
D.基层
47.为了（）2017年7月8日国务院出台了《新一代人工智能发展规划》，为推动我国人工智能的长期发展指明了方向。
D.军民融合
37.中共中央政治局于2017年12月8日下午就实施国家大数据战略进行（）集体学习。
A.第二次
38.根据《打造智慧社区，优化居家养老（中）》，下列哪项服务内容不包含在乌镇智慧养老强调的服务功能内容中（）。
人工智能课程大纲课程体系：《机器视觉技术》课程产品白皮书(2019V1.0)
《机器视觉技术》产品白皮书目录1引言........................................................................ - 3 -2产品概述.................................................................... - 4 -2.1产品体系............................................................ - 4 -2.2产品资源............................................................ - 5 -3产品介绍.................................................................... - 8 -3.1机器视觉技术........................................................ - 8 -3.1.1课程说明........................................................ - 8 -3.1.2教学大纲....................................................... - 12 -3.1.3教学指导....................................................... - 16 -4配套产品................................................................... - 19 -4.1实验设备........................................................... - 19 -4.2软件平台........................................................... - 24 -5技术支持................................................................... - 28 -5.1.1升级服务....................................................... - 28 -5.1.2师资培训....................................................... - 28 -1引言中国人工智能发展迅猛，中国政府也高度重视人工智能领域的发展。
百度大脑AI技术成果白皮书.doc
百度大脑AI技术成果白皮书i目录目录引言.1一、百度大脑进化到5.0.2二、基础层.32.1算法.32.2算力.52.3数据10三、感知层113.1语音113.2视觉133.3增强现实/虚拟现实.17四、认知层194.1知识图谱，科技与商业发展的一个关键词就是“人工智能”。
在近一年的时间里，百度科学家和工程师们不仅在人工智能算法、核心框架、芯片、计算平台、量子计算、语音技术、计算机视觉、增强现实与虚拟现实、语言与知识、开放平台、开放数据等诸多方面取得了令人瞩目的技术成果，还将这些技术成果与行业相结合，成功应用于众多产品之中，取得了丰硕的人工智能应用成果。
2月，世界知识产权组织（WorldIntellectualPropertyOrganization，简称WIPO）发布了首份技术趋势报告，聚焦人工智能领域专利申请及发展状况。
报告显示，百度在深度学习领域的专利申请量位居全球第二，超越Alphabet、微软、IBM等企业和国外学术机构，在全球企业中居于首位。
过去的一年，百度基础技术体系、智能云事业群组和AI技术平台体系进行了重大组织机构调整，三个体系统一向集团CTO 汇报，这为技术中台建设和人工智能技术落地提供了良好的组织保障。
本报告总结了百度大脑在度取得的部分技术成果：第一章主要概述百度大脑5.0，第二至六章分别介绍百度大脑在基础层、感知层、认知层、平台层和安全方面的技术成果。
面向未来，百度将继续打造领先的AI技术能力，构建更加繁荣的人工智能生态系统，助力各行各业进入智能化的工业大生产阶段，在智能时代创造更广泛的社会经济价值。
2一、百度大脑进化到一、百度大脑进化到5.0百度大脑是百度AI集大成者。
百度大脑自起开始积累基础能力，后逐步完善。
，百度大脑1.0完成了部分基础能力和核心技术对外开放；，2.0版形成了较为完整的技术体系，开放60多项AI能力；，3.0版在“多模态深度语义理解”上取得重大突破，同时开放110多项核心AI技术能力；，百度大脑升级为5.0，核心技术再获重大突破，实现了AI算法、计算架构与应用场景的创新融合，成为软硬件一体的AI大生产平台。
THM3060 用户手册
4 数字接口 ....................................................................................... 15
4.1 4.2 接口种类 ...................................................................................................................................... 15 SPI 模式 ...................................................................................................................................... 15 4.2.1 功能说明 ...................................................................................................................... 15 4.2.2 操作波形图 .................................................................................................................. 16 4.2.3 SPI 数据格式 .............................................................................................................. 16 4.2.4 SPI 操作示意 .............................................................................................................. 17 UART 接口 ................................................................................................................................. 18 4.3.1 UART 字符结构 ......................................................................................................... 19 4.3.2 UART 帧结构 ............................................................................................................. 19 4.3.3 UART 接口操作示意 ................................................................................................. 20 透明接口 ...................................................................................................................................... 21
SMIA_Characterisation_Specification_1.0
SMIA 1.0 Part 5: Camera Characterisation SpecificationDISCLAIMERThe contents of this document are copyright © 2004 Nokia Corporation, ST Microelectronics NV and their licensors. All rights reserved. You may not copy, modify nor distribute this document without prior written consent by Nokia and ST. No license to any Nokia’s, ST’s or their licensor’s intellectual property rights are granted herein.YOU ACKNOWLEDGE THAT THIS SMIA SPECIFICATION IS PROVIDED "AS IS" AND NEITHER NOKIA, ST NOR THEIR LICENSORS MAKE ANY REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE OR THAT THIS SMIA SPECIFICATION OR ANY PRODUCT, SOFTWARE APPLICATION OR SERVICE IMPLEMENTING THIS SMIA SPECIFICATION WILL NOT INFRINGE ANY THIRD PARTY PATENTS, COPYRIGHTS, TRADEMARKS OR OTHER RIGHTS. THERE IS NO WARRANTY BY NOKIA, ST OR BY ANY OTHER PARTY THAT THE FUNCTIONS CONTAINED IN THIS SMIA SPECIFICATION WILL MEET YOUR REQUIREMENTS.LIMITATION OF LIABILITY. IN NO EVENT SHALL NOKIA, ST OR THEIR EMPLOYEES, LICENSORS OR AGENTS BE LIABLE FOR ANY LOST PROFITS OR COSTS OF PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES, PROPERTY DAMAGE, PERSONAL INJURY, LOSS OF PROFITS, INTERRUPTION OF BUSINESS OR FOR ANY SPECIAL, INDIRECT, INCIDENTAL, ECONOMIC, COVER, PUNITIVE, OR CONSEQUENTIAL DAMAGES, HOWEVER CAUSED AND WHETHER ARISING UNDER CONTRACT, TORT, NEGLIGENCE, OR OTHER THEORY OF LIABILITY ARISING OUT OF THIS SMIA SPECIFICATION, EVEN IF NOKIA, ST OR THEIR LICENSORS ARE ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. IN THE EVENT THAT ANY EXCLUSION CONTAINED HEREIN SHALL BE HELD TO BE INVALID FOR ANY REASON AND NOKIA, ST OR THEIR LICENSORS BECOMES LIABLE FOR LOSS OR DAMAGE THAT MAY LAWFULLY BE LIMITED, SUCH LIABILITY SHALL BE LIMITED TO U.S.$50.Specifications mentioned in this publication are subject to change without notice.This document supersedes and replaces all versions previously supplied.HistoryVersion Date Author Status Notes1.0 30-June-04 Nokia and ST ApprovedTable of contentsSCOPE (11)1.Definitions (12)1.1Arrays (12)1.2Standard Parameters (13)1.2.1Raw Bayer Image Data (13)1.2.2Green (Red) Raw Bayer (13)1.2.3Region of Interest (ROI) (13)1.3Function Descriptions (15)1.3.1AV_IMAGE(#1, ..., #F) (15)1.3.2COLUMN_AV(#) (15)1.3.3CONVOLUTION(#1,#2) (16)1.3.4Extract Colour Plane - GREENRED (16)1.3.5LOG10(x) (16)1.3.6Local Deviation (16)1.3.7MAX(#) (16)1.3.8MEAN(#) (16)1.3.9MIN(#) (16)1.3.10Regression Analysis (17)1.3.11RMS(#) (17)1.3.12ROI(a,b;x,y;#) (17)1.3.13ROW_AV(#) (17)1.3.14STDEV(#) (17)2.Pre-Processing (18)2.1Data Pre-Processing (18)2.2Image Pre-Processing (18)2.2.1Introduction (18)2.2.2Kernel Kern k (18)2.2.3Error Detection (19)2.2.4Defect Correction (19)3.Test Equipment and Environmental Requirements (20)3.1General (20)3.2Illumination Specification (20)3.3Environmental Specification (20)3.4Electrical Specification (21)3.4.1Analogue Supply (21)3.4.2Digital Supply (21)4.Default Configuration (22)4.1Default Camera Configuration (22)4.1.1Default Electrical Conditions (22)4.1.2Camera Register Settings (22)4.1.3Default Environmental Conditions (22)4.1.4Default Lighting Conditions (22)4.2Default Characterisation Configuration (23)4.2.1Darkroom Set Up (23)4.2.2Mobile Device Equivalence Model (24)5.Characterisation Test Methods (25)5.1Dynamic Range (25)5.1.1Description (25)5.1.2Test Conditions (25)5.1.3Analysis (25)5.2Vertical Fixed Pattern Noise (26)5.2.1Description (26)5.2.2Test Conditions (27)5.2.3Analysis (27)5.3Horizontal Fixed Pattern Noise (28)5.3.1Description (28)5.3.2Test Conditions (28)5.3.3Analysis (29)5.4Temporal Noise (30)5.4.1Description (30)5.4.2Test Conditions (30)5.4.3Analysis (30)5.5Column Noise (31)5.5.1Description (31)5.5.2Test Conditions (31)5.5.3Analysis (32)5.6Row Noise (33)5.6.1Description (33)5.6.2Test Conditions (33)5.6.3Analysis (34)5.7Frame to Frame Flicker (35)5.7.1Description (35)5.7.2Test Conditions (35)5.7.3Analysis (35)5.8Dark Signal (36)5.8.1Description (36)5.8.2Test Conditions (36)5.8.3Analysis (36)5.9Dark Signal Non-uniformity (37)5.9.1Description (37)5.9.2Test Conditions (37)5.9.3Analysis (37)5.10Power Supply Rejection Ratio (38)5.10.1Description (38)5.10.2Test Conditions (39)5.10.3Analysis (40)5.11Signal to Noise Ratio (41)5.11.1Description (41)5.11.2Test Conditions (41)5.11.3Analysis (42)5.12Sensitivity (43)5.12.1Description (43)5.12.2Test Conditions (43)5.12.3Analysis (44)5.13Maximum Illumination (45)5.13.1Description (45)5.13.2Analysis (45)5.14Minimum Illumination (46)5.14.1Description (46)5.14.2Test Conditions (46)5.14.3Analysis (46)5.15Module Response Non-Linearity (47)5.15.1Description (47)5.15.2Test Conditions (48)5.15.3Analysis (49)5.16Photo-Response Non-Uniformity (50)5.16.1Description (50)5.16.2Test Conditions (50)5.16.3Analysis (51)5.17Relative Illumination (52)5.17.1Description (52)5.17.2Test Conditions (52)5.17.3Analysis (52)5.18Spatial Frequency Response (53)5.18.1Description (53)5.18.2Test Conditions (54)5.18.3Analysis (54)5.19Image Sharpness Measurement (56)5.19.1Description (56)5.19.2Test conditions (58)5.19.3Analysis (59)5.20TV Distortion (61)5.20.1Description (61)5.20.2Test Conditions (62)5.20.3Analysis (62)5.21Field of View (64)5.21.1Description (64)5.21.2Test Conditions (64)5.21.3Analysis (65)5.22Colour Accuracy (66)5.22.1Description (66)5.22.2Test Conditions (66)5.22.3Conversion of Data into CIELAB (67)5.22.4Conversion from sRGB into XYZ 1931 CIE (Tristimulus) Values (69)5.22.5Conversion from XYZ 1931 CIE (Tristimulus) Values to CIELAB (70)5.22.6Calculation of Colour/Hue Accuracy from L*a*b* (CIELAB) Data (70)5.22.7Pseudo Code for the Analysis Process (71)5.23Image Lag (74)5.23.1Description (74)5.23.2Test Conditions (74)5.23.3Analysis (75)5.24Veiling Glare (76)5.24.1Description (76)5.24.2Test Conditions (77)5.24.3Analysis (78)References (79)Appendix A- Default Test Conditions (80)Appendix B- Test Charts (81)Appendix C– Possible Trial-and-Error Image Lag Test Method (82)List of tablesTable 1: Acronyms (ix)Table 2: Definitions (ix)Table 3: ECR (x)Table 4: Bayer Matrix Orientation (13)Table 5: Standard ROIs (14)Table 6: Example ROI Dimensions (14)Table 7: Illumination Specification (20)Table 8: Environmental Specification (20)Table 9: Analogue Supply Specification (21)Table 10: Digital Supply Specification (21)Table 11: Default Electrical Conditions (22)Table 12: Default Environmental Conditions (22)Table 13: Default Lighting Conditions (22)Table 14: Dynamic Range Test Conditions (25)Table 15: Vertical Fixed Pattern Noise Test Conditions (27)Table 16: Horizontal Fixed Pattern Noise Test Conditions (28)Table 17: Temporal Noise Test Conditions (30)Table 18: Column Noise Test Conditions (31)Table 19: Row Noise Test Conditions (33)Table 20: Frame to Frame Flicker Test Conditions (35)Table 21: Dark Signal Test Conditions (36)Table 22: Dark Signal Non-Uniformity Test Conditions (37)Table 23: Power Supply Rejection Ratio Test Conditions (39)Table 24: Electrical Test Conditions (40)Table 25: Signal to Noise Ratio Test Conditions (41)Table 26: Sensitivity Test Conditions (43)Table 27: Minimum Illumination Test Conditions (46)Table 28: Module Response Non-Linearity Test Conditions (48)Table 29: Photo-Response Non-Uniformity Test Conditions (50)Table 30: Relative Illumination Test Conditions (52)Table 31: SFR Test Conditions (54)Table 32: Image Sharpness ROIs (57)Table 33: Image Sharpness Test Kernel (58)Table 34: Image Sharpness Test Conditions (58)Table 35: TV Distortion Test Conditions (62)Table 36: FOV Test Conditions (64)Table 37: Colour Accuracy Test Conditions (66)Table 38: Image Lag Test Conditions (74)Table 39: Veiling Glare Test Conditions (77)Table 40 - Default Test Conditions (80)List of figuresFigure 1: Taking a sub-matrix (12)Figure 2 - Measurement ROIs (15)Figure 3: Default Darkroom Set Up (23)Figure 4: Electrical Schematic of Mobile Device Equivalence Model (24)Figure 5: Image Sharpness ROIs For Example Chart (57)Figure 6: Distorted image of a square, showing pincushion distortion (61)Figure 7: Colour Test Image Capture Set Up (67)Figure 8: Colour Accuracy Process (68)Figure 9: Bayer Data to sRGB Conversion (68)Figure 10: Veiling Glare Measurement Set-up schematic (77)Figure 11: Image Lag Frame Acceptability (82)Acronyms Abbreviations and Definitions:Functional descriptions can be found in section 1.3.CCP Compact Camera PortCCI Camera Control InterfaceEMC Electro Magnetic CompatibilityEMI Electro Magnetic InterferenceFE Frame EndFps Frames per secondFS Frame StartFSD Full Scale DeflectionI2C Inter ICbusIF InterfaceIO Input/OutputLSB Least Significant ByteLVDS Low Voltage Differential SignallingMbps Megabits per secondMSB Most Significant ByteOECF Opto-Electronic Conversion FunctionPSRR Power Supply Rejection RatioRH Relative HumidityRO Read OnlyROI Region of InterestRW Read/WriteSCK System ClockSFR Spatial Frequency ResponseSMIA Standard Mobile Imaging ArchitectureSubLVDS Sub-Low Voltage Differential SignallingSVGA Super Video Graphics Array (800x600)VGA Video Graphics Array (640x480)Table 1: AcronymsFull scale deflection Taken to be the maximum pixel output minus the minimum pixeloutput (pedestal). Note that the maximum pixel output might notbe 2n-1 and the minimum pixel output is unlikely to be 0. Integration time Integration is the time in seconds between pixel reset and read. Optical axis Line through the centres of curvature of the surfaces of the opticalsystem.Pedestal Fixed offset used to compensate for black level of the cameramodule. The pedestal value is the offset from 0 codes to therequired black level.Table 2: DefinitionsPREFACESpecification Supersedes Earlier DocumentsThis document contains the SMIA Characterisation specification.Following publication of the SMIA Standard, there may be future approved errata and/or approved changes to the standard prior to the issuance of another formal revision.Incorporation of Engineering Change Requests (ECRs)The following ECRs have been incorporated into this version of the specification:ECR DESCRIPTIONTable 3: ECRSCOPEThis document describes the tests which are used to characterise the performance of a SMIA camera. In general, a test plan will be used to define the number of samples to be used for each test, and any deviations from the test methods and test conditions described in this specification.The document is arranged as follows:-• Chapter 1 Definitions. This provides standard definitions which are used throughout the document. These include array nomenclature, image data formats and function descriptions.• Chapter 2 Pre-Processing. This includes descriptions of data manipulation steps that are used prior to calculations on captured image data.• Chapter 3 Test Equipment and Environmental Requirements. This defines the capabilities of the equipment and environment required to make the measurements.• Chapter 4 Default Configuration. This describes the default camera configuration & physical darkroom set up for tests, and electrical schematic. Specific settings which deviate from the defaults are defined in each test method description.• Chapter 5 Characterisation Test Methods. This includes descriptions of each of the individual test methods. A standard template is used for each method, with the following parts:- o Overview of the test objective.o Formal description of the calculations required and the physical set up.o Table to define the test conditions (illumination, environmental, electrical, camer settings, capture method and pre-processing).o Pseudo code to describe the analysis required, using functions defined in the “Definitions” chapter.• Appendix A describes the default environmental, electrical supply and analogue gain test conditions at which each characterisation test is conducted. These should be used where a specific test plan has not been provided.• Appendix B provides information on suitable test charts for various tests, and references to the electronic versions.• Additional appendices are used for supplementary information .This specification includes descriptions for 24 characterisation tests. Additional tests for depth of focus, flare, ghosting, out of scene image artifacts, infra red response, blemish and EMC will be added to a future release of this specification.Nokia & ST Confidential Page 11 of 83Nokia & ST ConfidentialPage 12 of 831. DefinitionsCare should be taken to avoid loss of precision, for example due to rounding errors when performing calculations or by reducing the bit depth of the data.1.1 ArraysIn this document a generic array is a collection of values ordered in a 2-dimensional matrix. AThe size of is written where m is the number of columns and n is the number of rows. When the number of elements in the array is needed as a quantity, size may also be used as a function: . A n m ×mn A size =)(An individual value in may be referred to as an element, entry, item, member, pixel, position, value, etc. The value occurring on the i A th row and in the j th column of is written . The top left value is while the bottom right value is A ),(j i A )0,0(A )1,1(−−n m A . The sum of a matrix is the sum of all its elements: .∑∑−=−==101),()(m i n j j i A A sum A sub-array can be described using sets of values, e.g. )2,2(j i A B = where andrepresents an array one quarter the size of and taking it’s values from the 2m i <≤20n j <≤20A nd , 4th , 6thetc rows and 2nd , 4th , 6th etc columns. This is illustrated in Figure 1.a b c d e f …g h i j k la c e … m n o p q r …m o qs t u v w xy aa ac … y z aa ab acadB=: : ae af ag ah ai aj … A= :::Figure 1: Taking a sub-matrixFor convenience we also define the mean of an array as )()()()(A size A sum A A A mean ===µ, thevariance of an array as ∑∑−=−=−−=1012)),((1)(1)var(m i n j A j i A A size A . and the standard deviation of an array as )var()()(A A A std ==σ.Nokia & ST ConfidentialPage 13 of 831.2 Standard Parameters1.2.1 Raw Bayer Image DataThe precise data format of an SMIA compatible image is already given in the SMIA Functional Specification and consists of a single bit depth Bayer pixel array, with size where m and n are both even numbers. A n m ×Using this image data the following types of image arrays are required for the Optical Characterisation measurements.1.2.2 Green (Red) Raw BayerGreen (Red) Raw Bayer contains the visible Green pixels data from each row containing Green and Red Bayer pixels. This array can be written )2,2()(q j p i A A GR G ++= where m i <≤20,, and n j <≤20p and depend upon the alignment of the Bayer matrix as shown in the tablebelow.qFirst Bayer column contains blue pixelsFirst Bayer column contains red pixelsFirst Bayer row contains red pixelsp = 0, q = 0 p = 0, q = 1 First Bayer row contains blue pixelsp = 0, q = 1p = 1, q = 1Table 4: Bayer Matrix Orientation1.2.3 Region of Interest (ROI)A Region Of Interest (ROI) is a continuous sub-array of the form where and. For a given process one or more ROIs may be defined with algorithms being run just onthe ROI sub-arrays instead of on the whole data set. ),(j i A 21x i x ≤≤21y j y ≤≤Some standard ROIs are defined in Table 5.Nokia & ST ConfidentialPage 14 of 83Description of location Area relative to )(GR G A Sub-array of with size )(GR G A n m ×Range for columnsRange for rowsROI (1)Geometric centre 5⅓% *),()(j i A GR Gi k m ≤−12/ 12/1−+≤k m **j k n ≤−12/ 12/1−+≤k n **ROI (2)Geometric centre 1% ),()(j i A GR G 120/1120/9−≤≤m i m 120/1120/9−≤≤n j nROI (3)Upper left 1% ),()(j i A GR G 110/0−≤≤m i 110/0−≤≤n j ROI (4)Lower left 1% ),()(j i A GR G 110/9−≤≤m i m 110/0−≤≤n j ROI (5)Upper right 1% ),()(j i A GR G 110/0−≤≤m i 110/9−≤≤n j n ROI (6)Lower right1%),()(j i A GR G110/9−≤≤m i m110/9−≤≤n j nTable 5: Standard ROIs1.2.3.1 Example ROI DimensionsExample ROI dimensions are shown in Table 6.Module type Green-Red Bayer pixel dimensionsROI () dimensions)1(ROI ROI () dimensions)6,5,4,3,2(ROI SMIA VGA(640 x 480) 320 x 240 64 x 6432 x 24 SMIA SVGA(800 x 600)400 x 300 80 x 8040 x 30Table 6: Example ROI Dimensions*If this is less then 64x64 pixels then area is defined as 64 x 64 pixels**where 5/)3/(1mn k =Nokia & ST ConfidentialPage 15 of 831.2.3.2 Measurement Locations)(n iFigure 2 - Measurement ROIs1.3 Function Descriptions1.3.1 AV_IMAGE(#1, ..., #F)Takes a number of frames, F, to produce a composite image containing the average values for each pixel.Thus if A = AV_IMAGE(A 1, A 2, …, A F ) then ∑==Fk kj i A Fj i A 1),(1),( for each i,j where 0 ≤ i < m ,0 ≤ j < n .1.3.2 COLUMN_AV(#)Takes the column averages for a frame and outputs a row vector.Thus if C = COLUMN_AV(A) then ∑−==1),(1)(N j j i A n i C for each i where 0 ≤ i < m .Nokia & ST ConfidentialPage 16 of 831.3.3 CONVOLUTION(#1,#2)Convolves two arrays to produce a fresh array with the output being placed at the position in array #1 coincident with centre entry of the array #2.Thus if A is an m x n matrix, K is a r x s matrix (where r = 2u+1 and s = 2v+1), and B = CONVOLUTION(A, K) then for each i,j where 0 ≤ i < m , 0 ≤ j < n .∑∑−=−=++++=u u g vvh h v g u K h j g i A j i B ),(),(),(In cases when i + u < 0 or i + u ≥ m , and/or j + v < 0 or j + v ≥ n the kernel array K overhangs the edge of the array A and so A(i + g, j + h) is undefined for some values of g and h . In such cases define a sub-array C of A asC = A(e, f) where max(0, i - u) ≤ e ≤ min(m - 1, i + u) and max(0, j - v) ≤ f ≤ min(n - 1, j + v)and use A(i + g, j + h) = mean(C) when i + g < 0 or i + g ≥ m , and/or j + h < 0 or j + h ≥ n . 1.3.4 Extract Colour Plane - GREENREDThus GREENRED(A) = A G(GR) as defined in section 1.2.2. 1.3.5 LOG10(x)Logarithm to the base 10.1.3.6 Local DeviationThis takes the deviation of a point from the average of its locality.The local standard deviation is defined by∑∑−=−=⋅−⋅=1012,11M i N j j i local local N M δσ ,where()⎟⎟⎠⎞⎜⎜⎝⎛−⎟⎟⎠⎞⎜⎜⎝⎛⋅−+⋅−=∑∑+−=+−=j i K i K i n K j K j m m n j i ji local p p K p ,,2,,1121δ given p i,j is the pixel value at (i,j) and K is the locality parameter. For most cameras the localityparameter can be set to K = 5, which yields an average over 120 pixels for the locality. When dealing with pixels at the edge of the array, for calculation purposes the pixels outside the array assume the value of the average of those inside the array and the locality. 1.3.7 MAX(#)Finds the maximum entry value in an array.1.3.8 MEAN(#)Finds the mean of the entry values in an array, i.e. MEAN(A) = mean(A) as defined in section 1.1. 1.3.9 MIN(#)Finds the minimum entry value in an array.Nokia & ST ConfidentialPage 17 of 831.3.10 Regression AnalysisSome methods give rise to a set of pairs of measured values (x 1,y 1), (x 2,y 2), …, (x n ,y n ). We can draw a best fit straight line y=mx+c through these points using the Gaussian method of least squares by setting m and c as follows.Let∑==n i i x n x 11 and ∑==n i iy n y 11. Also let∑=−−−=n i i i xy y y x x n s 1))((11 and ∑=−−=n i i x x n s 1221)(11. Then21s s m xy= and x m y c −=.1.3.11 RMS(#)Finds the root mean squared of array. Thus ∑∑−=−==1012),()(1)(m i n j j i A A size A RMS , where size(A) is defined in section 1.1.1.3.12 ROI(a,b;x,y;#)ROI extracts a region of interest of size (a,b) with top left coordinates (x,y) from an array and outputs the ROI as a new arrayThus ROI(a,b;x,y;A) = A(x + i, y + j) for each i,j where 0 ≤ i < a , 0 ≤ j < b . 1.3.13 ROW_AV(#)Takes the row averages for a frame and outputs a column vector.Thus if R = ROW_AV(A) then ∑−==1),(1)(m i j i A m j R for each j where 0 ≤ j < n . 1.3.14 STDEV(#)Finds the standard deviation of an array's entry values. Note that this is always the sample standard deviation σn-1 and this is the quantity referred to as standard deviation in the text, i.e. STDEV(A) = std(A) as defined in section 1.1.Nokia & ST ConfidentialPage 18 of 832. Pre-Processing2.1 Data Pre-ProcessingThe SMIA Functional Specification describes the format of the data output by SMIA cameras, and should be used to correctly unpack the captured image data.Additionally, it describes Data Pedestal. Each characterisation test method in section 5 states whether the data pedestal should be subtracted from the unpacked data. Data pedestal subtraction is performed by subtracting the pedestal from each pixel value, clipping to zero if the original pixel value is less than the pedestal.Thus if A = Pedestal_Offset_Subtraction(F) then A(i, j) = F(i, j ) - p if F(i, j ) >p otherwise A(i, j) = 0, for each i,j where 0 ≤ i < m , 0 ≤ j < n , and where p is the pedestal value in codes.2.2 Image Pre-Processing2.2.1 IntroductionIn normal use, a camera module can be expected to operate with a large amount of digital signal processing to remove errors and enhance the overall image quality. As a SMIA camera module is characterised using raw Bayer data a certain amount of low-level error detection and defect correction is necessary for the majority of tests.We use a measurement Kernel of size (see Section 2.2.2), which traverses each image pixel in the image array from top left through to bottom right. The default value of k is 1. The result of this convolution creates a corrected image data array (see Sections 2.2.3, 2.2.4) based on the value of the central pixel of the Kernel compared to its surrounding pixels. If a pixel is close to the edge of the array its Kernel may extend past the array boundaries. In such cases the value of the corresponding pixel in the corrected array is set to the value of the pixel in the original array. This new corrected array is then used for later analysis. k Kern k 2.2.2 Kernel Kern kThe kernel of size used in section 2.2 is a square array of size with equal weightings on for each entry (i.e. all entries in the kernel parameter of the convolution are set to 1). k Kern k 1212+×+k kOther kernels are also used elsewhere in the text and are described explicitly when required.Given an array , the kernel of the pixel is the sub-array whereand A ),(y x A ),()),((j i A y x A Kern k =k x i k x +≤≤−k y j k y +≤≤−.Nokia & ST ConfidentialPage 19 of 832.2.3 Error DetectionThe purpose of this routine is to detect pixels defects on the image by recording the difference between the local pixel value and the kernel mean. This may be used at a later date for blemish test method, but is currently not used.k K For each pixel let , let ),(y x A )),((y x A Kern Kern k =1)(),()(−−=Kern size y x A Kern sum aThen create a new array with ected errors A det _a y x A y x A ected errors −=),(),(det _.2.2.4 Defect CorrectionThe purpose of this correction routine is to remove the effects of large pixels defects on the images by setting any pixel with a value that is +/-15% of FSD deviation from the Kernel mean (Kern ).For each pixel let , let ),(y x A )),((y x A Kern Kern k =1)(),()(−−=Kern size y x A Kern sum aand create a new array where if corrected defect A _15.0)),((×<−FSD a y x A abs thenotherwise ),(),(_y x A y x A corrected defect =a y x A corrected defect =),(_.3. Test Equipment and Environmental Requirements3.1 GeneralAll test results should state the measurement accuracy achieved with the measurement equipment used. The following sections specify the capability of the equipment required to make the measurements, not the actual measurement conditions.3.2 Illumination SpecificationIllumination type TungstenHalogenTungstenD65 D75 DiffuseColour temperature 3200-3400K2500-3000K6500K 7500K2500-3400KType TungstenhalogenTungstenDaylightFluorescentFluorescentTungsten ortungstenhalogenElectrical SupplyFrequency DC DC 20 – 100kHz 20 – 100kHzIntensity range atchart10 - 2000 Lux1 - 2000 Cd/m2> 100 Lux> 100 Lux > 100 Lux> 50 Lux atdiffuserAngle of incidence of eachlight source (with respect to chart) 45º 45º 45º 45ºDiffuseUniformity ofillumination atchart± 5% ± 5% ± 5% ± 5% ± 2%Table 7: Illumination SpecificationAdditionally, the “Dark” condition is defined as one in which no detectable light (< 1mLux) can reach the camera. It is recommended that a double shielding approach is taken. For instance, the camera is covered by a black cap and blackout cloth in a darkroom.3.3 Environmental SpecificationParameter Value Tolerance UnitsTemperature rangeMinimum Maximum -30+70± 1%± 1%°C°CHumidity <70 ± 5% % RHTable 8: Environmental SpecificationNokia & ST Confidential Page 20 of 833.4 Electrical Specification3.4.1 Analogue SupplyParameter Minimum Typical Maximum UnitsVoltsVoltage 0.0 2.8 5.0 DCCurrent(Resistive) +/-50 mATable 9: Analogue Supply Specification3.4.2 Digital SupplyParameter Minimum Typical Maximum UnitsVoltsVoltage 0.0 1.8 5.0 DCCurrent(Resistive) +/-50 mATable 10: Digital Supply SpecificationNokia & ST Confidential Page 21 of 834. Default Configuration4.1 Default Camera Configuration4.1.1 Default Electrical ConditionsParameter Value Tolerance UnitsReference Analogue supply (VANA) 2.8 ± 0.1 DC Volts SMIA Functional SpecificationModulation OFFDigital supply (VDIG) 1.8 ± 0.1 DC Volts SMIA Functional SpecificationModulation OFFExternal Clock (EXTCLK) SMIA Functional SpecificationFrequency 13.0 ±0.1 MHzLevel V DIG- VoltsTable 11: Default Electrical Conditions4.1.2 Camera Register SettingsThe SMIA camera shall be reset before each Characterisation test is performed so that the camera registers contain the default data defined in the SMIA Functional Specification.Additionally, the camera registers should be configured for• 13MHz External Clock, unless specified in the test plan• Analogue gain specified for the test• Digital gain specified for the test• Integration time required for the test• Frame Rate required for the test• Other camera-specific registersThe default frame rate is the lesser of 15fps or the maximum achievable frame rate.Information on the register settings used for each test should be supplied with the test results.4.1.3 Default Environmental ConditionsParameter Value Tolerance UnitsTemperature23 ±2 °CHumidity <70 %RHTable 12: Default Environmental Conditions4.1.4 Default Lighting ConditionsParameter ValueTolerance UnitsIlluminationType Intensity at chartUniformity Tungsten Halogen300± 5%----Lux-Table 13: Default Lighting ConditionsNokia & ST Confidential Page 22 of 83。
超融合技术白皮书
深信服超融合架构技术白皮书深信服科技有限公司修订记录深信服超融合架构技术白皮书文档密级：内部第1章、前言 (8)1.1IT时代的变革 (8)1.2白皮书总览 (9)第2章、深信服超融合技术架构 (11)1.1超融合架构概述 (11)1.1.1超融合架构的定义 (11)1.2深信服超融合架构组成模块 (11)1.2.1.1系统总体架构 (11)1.2.1.2aSV计算虚拟化平台 (12)1.2.1.2.1概述 (12)1.2.1.2.2aSV技术原理 (13)1.2.1.2.2.1aSV的Hypervisor架构 (14)1.2.1.2.2.2Hypervisor虚拟化实现 (17)1.2.1.2.3aSV的技术特性 (25)1.2.1.2.3.1内存NUMA技术 (25)1.2.1.2.3.2SR-IOV (26)1.2.1.2.3.3Faik-raid (27)1.2.1.2.3.4虚拟机生命周期管理 (28)1.2.1.2.3.5虚拟交换机 (29)1.2.1.2.3.6动态资源调度 (30)1.2.1.2.4aSV的特色技术 (30)1.2.1.2.4.1快虚 (30)1.2.1.2.4.2虚拟机热迁移 (31)1.2.1.2.4.3虚拟磁盘加密 (32)1.2.1.2.4.4虚拟机的HA (33)1.2.1.2.4.5多USB映射 (33)1.2.1.3aSAN存储虚拟化 (35)1.2.1.3.1存储虚拟化概述 (35)1.2.1.3.1.1虚拟后对存储带来的挑战 (35)1.2.1.3.1.2分布式存储技术的发展 (35)1.2.1.3.1.3深信服aSAN概述 (36)1.2.1.3.2aSAN技术原理 (36)1.2.1.3.2.1主机管理 (36)1.2.1.3.2.2文件副本 (37)1.2.1.3.2.3磁盘管理 (38)1.2.1.3.2.4SSD读缓存原理 (39)1.2.1.3.2.5SSD写缓存原理 (45)1.2.1.3.2.6磁盘故障处理机制 (49)1.2.1.3.3深信服aSAN功能特性 (60)1.2.1.3.3.1存储精简配置 (60)1.2.1.3.3.2aSAN私网链路聚合 (61)1.2.1.3.3.3数据一致性检查 (61)1.2.1.4aNet网络虚拟化 (61)1.2.1.4.1网络虚拟化概述 (61)1.2.1.4.2aNET网络虚拟化技术原理 (62)1.2.1.4.2.1SDN (62)1.2.1.4.2.2NFV (63)1.2.1.4.2.3aNet底层的实现 (64)1.2.1.4.3功能特性 (68)1.2.1.4.3.1aSW分布式虚拟交换机 (68)1.2.1.4.3.2aRouter (68)1.2.1.4.3.3vAF (69)1.2.1.4.3.4vAD (69)1.2.1.4.4深信服aNet的特色技术 (69)1.2.1.4.4.1网络探测功能 (69)1.2.1.4.4.2全网流量可视 (70)1.2.1.4.4.3所画即所得业务逻辑拓扑 (70)1.2.2深信服超融合架构产品介绍 (71)1.2.2.1产品概述 (71)1.2.2.2产品定位 (71)第3章、深信服超融合架构带来的核心价值 (73)1.1可靠性： (73)1.2安全性 (73)1.3灵活弹性 (73)1.4易操作性 (73)第4章、超融合架构最佳实践 (74)第1章、前言1.1 IT时代的变革20 世纪90 年代，随着Windows 的广泛使用及Linux 服务器操作系统的出现奠定了x86服务器的行业标准地位，然而x86 服务器部署的增长带来了新的IT 基础架构和运作难题，包括：基础架构利用率低、物理基础架构成本日益攀升、IT 管理成本不断提高以及对关键应用故障和灾难保护不足等问题。
中国联通：算力网络白皮书
目录1 产业背景 (1)1.1机器智能社会将全面到来 (1)1.2网络将出现云、边、端三级算力架构 (2)1.3实现云、边、端算力的高效需要算力网络 (2)1.4运营商的可持续发展需要算力网络 (4)2 算力网络的概念和架构 (7)2.1算力网络是云化网络发展演进的下一个阶段 (7)2.2算力网络的关键技术元素 (8)2.2.1联网元素：打造无损和确定性的网络联接 (9)2.2.2云网元素：智能网络与网络云化的持续推进 (10)2.2.3算网元素：为计算服务的可信、高效、随需网络 (10)2.3算力网络的典型应用场景 (16)2.3.1运营商ToB的“5G园区+AI”场景 (16)2.3.2运营商ToC的“5G+Cloud X”场景 (17)2.3.3算力开放，运营商提供可交易的算力通证 (18)3 算力网络的标准与生态 (19)4 总结与展望 (20)5 缩略语 (22)1产业背景1.1机器智能社会将全面到来人类将步入智能社会，智能是知识和智力的总和，翻译到数字世界就是“数据+算力+算法”，其中算法需要通过科学家研究实现，海量数据来自于各行各业的人和物，数据的处理需要大量算力，算力是智能的基础平台，由大量计算设备组成。
图1-1 智能的三要素：算法、算力、数据人脑的算力相当于约300亿颗晶体管，人类的历史和文明都是由无数人脑算力所创造，但人脑算力正面临老龄化的挑战，2020年超高龄国家（65岁以上人口超过总人口20%）将达到13个，2030年将上升到34个，而且主要集中在亚太、欧美等较发达国家。
考虑到儿童占比约15%，实际这些国家的劳动适龄人口只占不到60%。
全球2020年人口约77亿，较发达国家人口约30亿，所以这些国家实际处于劳动适龄段的人脑只有不到20亿。
现阶段电子工艺可以做到的机器算力已经接近人脑算力，如麒麟980基于7nm工艺集成了69亿晶体管，AMD Radeon VII GPU将采用7nm工艺，晶体管数量约132亿，未来5年，基于5nm工艺，芯片集成度据信可以做到300亿晶体管，此时处理器的信息分析处理能力已经与人脑相当，并且相比于人脑，处理器更聚焦于专业领域的数据处理，不知疲劳，所以在具体的数据处理领域，高端CPU的算力已经事实上相当于甚至于超过了人脑。
2018智能投顾白皮书
智能投顾，是IT科技和金融领域相结合的前沿应用领域。
它能够基于对投资者的精准画像，通过将现代金融理论融入人工智能算法，从而为投资者提供基于多元化资产的个性化、智能化、自动化和高速化的投资服务。
自2008年金融危机后，美国首家智能投顾公司Betterment于当年成立，随后Wealthfront，Personal Capital，Future Advisor，Motif Investing等创新型公司相继成立。
目前, 先锋集团（VanguardGroup）推出了VPAS，嘉信理财推出了SIP，富达基金推出了Fidelity Go，美林证券推出了Merrill Edge，摩根士丹利推出了Access Investing，“华尔街之狼- Kensho”推出了Warren。
智能投顾作为金融科技（FinTech）应用的最前沿领域，正席卷美国传统金融界。
发源于美国的智能投顾科技理论和技术西行东渐，我国智能投顾于2015年开始起步，虽然起步较晚，但是发展迅速。
招商银行推出了“摩羯智投”，工商银行推出了“AI投”，中国银行推出了“中银慧投”，平安银行推出了“平安智投”，兴业银行推出了“兴业智投”，广发证券推出了“贝塔牛”，平安证券推出了“AI慧炒股”，长江证券推出了“阿凡达”，京东集团推出了“京东智投”，羽时金融推出了“AI股”和“AI投顾”。
代表IT最新最前沿的人工智能技术在融入了金融行业后，有力地推进了传统金融行业的变革，有力地践行了普惠金融的理念。
虽然国内智能投顾的发展势头兴旺，但是商业模式不清晰，行业内鱼龙混杂，很多打着智能投顾概念的传统公司混杂其中，让人难以明辨。
什么是智能投顾？智能投顾的国内外发展现状如何？国内智能投顾业务的发展面临哪些问题和挑战？作为新生事物，智能投顾的IT技术路线，智能投顾的商业模式，智能投顾的风险控制，智能投顾业务的国内外监管政策比较，如何界定智能投顾公司的业务边界，采用哪些方向的标准评价智能投顾公司，智能投顾未来的发展趋势，针对这些大家关心的焦点问题，《2018智能投顾行业白皮书》希望能为大家作出一些抛砖引玉的探讨。
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杉
中心介绍
北京未来芯片技术高精尖创新中心成立于 2015 年 10 月，是北京 市教委首批认定的“北京高等学校高精尖创新中心”之一。中心充分发 挥清华大学的学科、科研和人才优势，联合校内多个院系资源，组建了 微电子、光电子及柔性集成、微系统、类脑计算、基础前沿、综合应用 六个分中心以及微纳技术支撑平台。中心主任由清华大学副校长尤政院 士担任。中心以服务国家创新驱动发展战略和北京市全国科技创新中心 建设为出发点，致力于打造国家高层次人才梯队、全球开放型微纳技术 支撑平台，聚焦具有颠覆性创新的关键器件、芯片及微系统技术，推动 未来芯片产业实现跨越式发展。
算法
芯片
器件
高带宽片外存储器：HBM、DRAM、高速 GDDR、LPDDR、STT-MRAM…… 高速互联：SerDes，光互联通信 仿生器件（人工突触，人工神经元） ：忆阻器 新型计算器件：模拟计算，内存计算（in-memory computing） 片上存储器（突触阵列） ：分布式 SRAM、ReRAM、PCRAM 等 CMOS 工艺：工艺节点（16, 7, 5 nm） CMOS 多层集成：2.5D IC/SiP、3D-stack 技术、monolithic 3D 等 新型工艺：3D NAND, Flash Tunneling FETs、FeFET、FinFET 应用需求驱动 理论创新驱动
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北京未来芯片技术高精尖创新中心
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AI 芯片的关键特征
2.1 技术总述
目前，关于 AI 芯片的定义并没有一个严格和公认的标准。比较宽泛的看法是，面向人工智能应用的芯 片都可以称为 AI 芯片。时下，一些基于传统计算架构的芯片和各种软硬件加速方案相结合，在一些 AI 应 用场景下取得了巨大成功。但由于需求的多样性， 很难有任何单一的设计和方法能够很好地适用于各类情况。 因此，学界和业界涌现出多种专门针对人工智能应用的新颖设计和方法，覆盖了从半导体材料、器件、电 路到体系结构的各个层次。 本文探讨的 AI 芯片主要包括三类，一是经过软硬件优化可以高效支持 AI 应用的通用芯片，例如 GPU ； 二是侧重加速机器学习（尤其是神经网络、深度学习）算法的芯片，这也是目前 AI 芯片中最多的形 式； 三是受生物脑启发设计的神经形态计算芯片。 AI 技术是多层面的，贯穿了应用、算法机理、芯片、工具链、器件、工艺和材料等技术层级。各个层 级环环紧扣形成 AI 的技术链，如图表 2-1 所示。AI 芯片本身处于整个链条的中部，向上为应用和算法提 供高效支持，向下对器件和电路、工艺和材料提出需求。一方面，应用和算法的快速发展，尤其是深度学习、 卷积神经网络对 AI 芯片提出了 2-3 个数量级的性能优化需求，引发了近年来 AI 片研发的热潮。另一方面，
1.2 内容与目的
本文主要包括九方面内容 ： 第 1 章为发展 AI 芯片产业的战略意义以及白皮书基本内容概述。第 2 章综 述了 AI 芯片的技术背景，从多个维度提出了满足不同场景条件下 AI 芯片和硬件平台的关键特征。第 3 章 介绍近几年的 AI 芯片在云侧、边缘和终端设备等不同场景中的发展状况，总结了云侧和边缘设备需要解决 的不同问题，以及云侧和边缘设备如何协作支撑 AI 应用。第 4 章在 CMOS 工艺特征尺寸逐渐逼近极限的 大背景下，结合 AI 芯片面临的架构挑战，分析 AI 芯片的技术趋势。第 5 章讨论了建立在当前 CMOS 技 术集成上的云端和终端 AI 芯片架构创新。第 6 章主要介绍对 AI 芯片至关重要的存储技术，包括传统存储 技术的改进和基于新兴非易失存储（NVM）的存储器解决方案。第 7 章重点讨论在工艺、器件、电路和存 储器方面的前沿研究工作，和以此为基础的存内计算、生物神经网络等新技术趋势。第 8 章介绍神经形态 计算技术和芯片的算法、模型以及关键技术特征，并分析该技术面临的机遇和挑战。第 9 章主要讨论 AI 芯 片的基准测试和技术路线图的相关问题。第 10 章展望 AI 芯片的未来。 在人工智能热潮面前，本文一方面希望与全球学术和工业界分享 AI 芯片领域的创新成果 ； 另一方面希 望通过对 AI 芯片的技术认知和需求的深入洞察，帮助相关人士更加清晰地了解 AI 芯片所处的产业地位、发 展机遇与需求现状，通过对 AI 芯片产业现状及各种技术路线的梳理，增进对未来风险的预判。目前人工智 能技术整体发展仍处于初级阶段，未来还有很多技术和商业层面的挑战。我们要去除在产业发展过程中一窝 蜂“逐热而上”的虚火，在充满信心、怀抱希望的同时，保持冷静和客观，推动 AI 芯片产业可持续发展。
工艺
图表 2-1 AI 芯片相关技术概览
新型材料、工艺和器件的迅速发展，例如 3D 堆叠内存，工艺演进等也为 AI 芯片提供了显著提升性能和降 低功耗的可行性，这个推动力来源于基础研究的突破。总体而言，这两类动力共同促进了 AI 芯片技术近年 来的快速发展。
23 23 24 24 25 26 27 28 29 29 30 31 31 32 33 35 37 40
7 7.1 7.2 7.3 7.4 7.5 8 8.1 8.2 8.2.1 8.2.2 8.2.3 8.2.4 8.3 9 10
新兴计算技术 近内存计算 存内计算（In-memory Computing） 基于新型存储器的人工神经网络 生物神经网络 对电路设计的影响 神经形态芯片 神经形态芯片的算法模型 神经形态芯片的特性 可缩放、高并行的神经网络互联 众核结构 事件驱动 数据流计算 机遇与挑战 AI 芯片基准测试和发展路线图 展望未来 参考文献 索引
刘勇攀 杨建华 杨美基 汪 陈 IEEE Fellow 吴臻志 玉 IEEE Fellow IEEE Fellow IEEE Fellow IEEE Fellow IEEE Fellow IEEE Fellow IEEE Fellow 安 张孟凡 陈怡然 郑光廷 胡晓波 唐 黄汉森 凡德·斯皮格尔 谢 源 （20源自8）人工智能芯片技术白皮书
White Paper on AI Chip Technologies
目录
北京未来芯片技术高精尖创新中心
01 01 02 03 03 04 05 05 06 06 06 07 08 09 10 11 12 13 15 15 17 18 19 20 20 21 22
北京未来芯片技术高精尖创新中心
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2. AI 芯片的关键特征
应用
视频图像类：人脸识别、目标检测、图像生成、视频分析、视频审核、图像美化、以图搜图、AR…… 声音语音类：语音识别、语音合成、语音唤醒、声纹识别、乐曲生成、智能音箱、智能导航…… 文本类：文本分析、语言翻译、人机对话、阅读理解、推荐系统…… 控制类：自动驾驶、无人机、机器人、工业自动化…… 神经网络互联结构：多层感知机（MLP） 、卷积神经网络（CNN） 、循环神经网络（RNN） 、长短时记忆（LSTM） 网络、脉冲神经网络（SNN）…… 深度神经网络系统结构：AlexNet、ResNet、VGGNet、GoogLeNet…… 神经网络算法：反向传播算法、迁移学习、强化学习、One-shot learning、对抗学习、神经图灵机、脉冲时间依 赖可塑（STDP）…… 机器学习算法：支持向量机（SVM） 、K 近邻、贝叶斯、决策树、马尔可夫链、Adaboost、WordEmbedding…… 算法优化芯片：效能优化，低功耗优化，高速优化，灵活度优化，如深度学习加速器，人脸识别芯片…… 神经形态芯片：仿生类脑，生物脑启发，脑机制模拟…… 可编程芯片：考量灵活度，可编程性，算法兼容性，通用软件兼容，如 DSP、GPU、FPGA…… 芯片系统级结构：多核、众核、SIMD、运算阵列结构、存储器结构、片上网络结构、多片互联结构、内存接口、 通信结构、多级缓存…… 开发工具链 ： 编程框架（Tensorflow，caffe）衔接、编译器、仿真器、优化器（量化、裁剪） 、原子操作（网络）库……
人工智能芯片技术白皮书（2018）
编写委员会主席
尤 政 中国工程院院士 IEEE Fellow 清华大学 清华大学 魏少军
编写委员会副主席
吴华强 邓 宁 清华大学 清华大学
编写委员会成员（按姓氏笔划排序）
尹首一 王 朱 玲 晶 清华大学 清华大学 北京半导体行业协会 清华大学 马萨诸塞大学 香港应用科技研究院 清华大学 清华大学 台湾新竹清华大学 半导体研究联盟 杜克大学 香港科技大学 圣母大学 新思科技 斯坦福大学 宾夕法尼亚大学 加利福尼亚大学圣巴巴拉分校
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前言
1.1 背景与意义
人工智能 (Artificial Intelligence， 英文缩写为 AI)， 是研究、 开发用于模拟、 延伸和扩展人类智能的理论、 方法、技术及应用系统的一门科学技术。人工智能的本质是对人类思维过程的模拟。从 1956 年正式提出 “人工智能”概念算起，在半个多世纪的发展历程中，人们一直在这一领域进行长期的科学探索和技术攻坚， 试图了解智能的实质。和任何曾经处于发展过程中的新兴学科一样，人工智能早期发展并非一帆风顺，它 曾受到多方质疑，不断经历起伏。近些年，大数据的积聚、理论算法的革新、计算能力的提升及网络设施 的演进，使得持续积累了半个多世纪的人工智能产业又一次迎来革命性的进步，人工智能的研究和应用进 入全新的发展阶段。 当前， 人工智能正逐渐发展为新一代通用技术， 加快与经济社会各领域渗透融合， 已在医疗、 金融、 安防、 教育、交通、物流等多个领域实现新业态、新模式和新产品的突破式应用，带动生产流程、产品、信息消 费和服务业的智能化、高附加值转型发展。人工智能已处于新科技革命和产业变革的核心前沿，成为推动 经济社会发展的新引擎。 实际上，人工智能产业得以快速发展，无论是算法的实现、海量数据的获取和存储还是计算能力的体 现都离不开目前唯一的物理基础——芯片。可以说， “无芯片不 AI” ，能否开发出具有超高运算能力、符合 市场需求的芯片，已成为人工智能领域可持续发展的重要因素。
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