LEADER 04867nam 2200589 450 001 9910155575903321 005 20221206104126.0 010 $a1-62705-644-0 024 7 $a10.2200/S00736ED1V01Y201609EET008 035 $a(CKB)4340000000027785 035 $a(MiAaPQ)EBC4764785 035 $a(CaBNVSL)swl00406952 035 $a(OCoLC)965303474 035 $a(IEEE)7791116 035 $a(MOCL)201609EET008 035 $a(EXLCZ)994340000000027785 100 $a20161223h20172017 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 10$aNon-volatile in-memory computing by spintronics /$fHao Yu, Leibin Ni, Yuhao Wang 210 1$a[San Rafael, California] :$cMorgan & Claypool Publishers,$d2017. 210 4$dİ2017 215 $a1 online resource (163 pages) $ccolor illustrations 225 0 $aSynthesis Lectures on Emerging Engineering Technologies,$x2381-1439 300 $aPart of: Synthesis digital library of engineering and computer science. 311 $a1-62705-294-1 320 $aIncludes bibliographical references and index. 327 $a1. Introduction -- 1.1 Memory wall -- 1.2 Traditional semiconductor memory -- 1.2.1 Overview -- 1.2.2 Nano-scale limitations -- 1.3 Non-volatile spintronic memory -- 1.3.1 Basic magnetization process -- 1.3.2 Magnetization damping -- 1.3.3 Spin-transfer torque -- 1.3.4 Magnetization dynamics -- 1.3.5 Domain wall propagation -- 1.4 Traditional memory architecture -- 1.5 Non-volatile in-memory computing architecture -- 1.6 References -- 327 $a2. Non-volatile spintronic device and circuit -- 2.1 SPICE formulation with new nano-scale NVM devices -- 2.1.1 Traditional modified nodal analysis -- 2.1.2 New MNA with non-volatile state variables -- 2.2 STT-MTJ device and model -- 2.2.1 STT-MTJ -- 2.2.2 STT-RAM -- 2.2.3 Topological insulator -- 2.3 Domain wall device and model -- 2.3.1 Magnetization reversal -- 2.3.2 MTJ resistance -- 2.3.3 Domain wall propagation -- 2.3.4 Circular domain wall nanowire -- 2.4 Spintronic storage -- 2.4.1 Spintronic memory -- 2.4.2 Spintronic readout -- 2.5 Spintronic logic -- 2.5.1 XOR -- 2.5.2 Adder -- 2.5.3 Multiplier -- 2.5.4 LUT -- 2.6 Spintronic interconnect -- 2.6.1 Coding-based interconnect -- 2.6.2 Domain wall-based encoder/decoder -- 2.6.3 Performance evaluation -- 2.7 References -- 327 $a3. In-memory data encryption -- 3.1 In-memory advanced encryption standard -- 3.1.1 Fundamental of AES -- 3.1.2 Domain wall nanowire-based AES computing -- 3.1.3 Pipelined AES by domain wall nanowire -- 3.1.4 Performance evaluation -- 3.2 Domain wall-based SIMON block cipher -- 3.2.1 Fundamental of SIMON block cipher -- 3.2.2 Hardware stages -- 3.2.3 Round counter -- 3.2.4 Control signals -- 3.2.5 Key expansion -- 3.2.6 Encryption -- 3.2.7 Performance evaluation -- 3.3 References -- 327 $a4. In-memory data analytics -- 4.1 In-memory machine learning -- 4.1.1 Extreme learning machine -- 4.1.2 MapReduce-based matrix multiplication -- 4.1.3 Domain wall-based hardware mapping -- 4.1.4 Performance evaluation -- 4.2 In-memory face recognition -- 4.2.1 Energy-efficient STT-MRAM with Spare-represented data -- 4.2.2 QoS-aware adaptive current scaling -- 4.2.3 STT-RAM based hardware mapping -- 4.2.4 Performance evaluation -- 4.3 References -- Authors' biographies. 330 3 $aExa-scale computing needs to re-examine the existing hardware platform that can support intensive data-oriented computing. Since the main bottleneck is from memory, we aim to develop an energy-efficient in-memory computing platform in this book. First, the models of spin-transfer torque magnetic tunnel junction and racetrack memory are presented. Next, we show that the spintronics could be a candidate for future data-oriented computing for storage, logic, and interconnect. As a result, by utilizing spintronics, in-memory-based computing has been applied for data encryption and machine learning. The implementations of in-memory AES, Simon cipher, as well as interconnect are explained in details. In addition, in-memory-based machine learning and face recognition are also illustrated in this book. 410 0$aSynthesis digital library of engineering and computer science. 410 0$aSynthesis lectures on emerging engineering technologies ;$v# 8.$x2381-1439 606 $aSpintronics 606 $aNonvolatile random-access memory 615 0$aSpintronics. 615 0$aNonvolatile random-access memory. 676 $a621.381 700 $aYu$b Hao$0999569 702 $aNi$b Leibin 702 $aWang$b Yuhao 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910155575903321 996 $aNon-volatile in-memory computing by spintronics$92965167 997 $aUNINA LEADER 01095nam a2200253 i 4500 001 991000045919707536 005 20020503113119.0 008 981106s1966 it ||| | ita 035 $ab10018426-39ule_inst 035 $aocm00009330$9ExL 040 $aDip.to Beni Culturali$bita 111 2 $aConvegno di studi sulla Magna Grecia <5. ; 1965 ; Taranto>$0391303 245 10$aFilosofia e scienze in Magna Grecia :$batti del quinto convegno di studi sulla Magna Grecia, Taranto, 10-14 ottobre 1965 260 $aNapoli :$bL'arte tipografica,$c1966 300 $a339 p. ;$c24 cm 650 4$aCongressi$zTaranto$y1965 650 4$aMagna Grecia$xCongressi$y1965 907 $a.b10018426$b05-06-07$c31-05-02 912 $a991000045919707536 945 $aLE001 M I 5$g1$i2001000190188$lle001$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i1002136x$z31-05-02 945 $aLE016$g1$i2016000001853$lle016$on$pE15.00$q-$rn$so $t0$u0$v0$w0$x0$y.i14478079$z05-06-07 996 $aFilosofia e scienze in Magna Grecia$9178881 997 $aUNISALENTO 998 $ale001$ale016$b01-01-98$cm$da $e-$fita$git $h0$i1 LEADER 04902nam 22006495 450 001 9910300533903321 005 20200706042205.0 010 $a3-319-78732-2 024 7 $a10.1007/978-3-319-78732-9 035 $a(CKB)4100000003359609 035 $a(MiAaPQ)EBC5355975 035 $a(DE-He213)978-3-319-78732-9 035 $a(PPN)226697584 035 $a(EXLCZ)994100000003359609 100 $a20180420d2018 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApproximate Quantum Markov Chains /$fby David Sutter 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (124 pages) 225 1 $aSpringerBriefs in Mathematical Physics,$x2197-1757 ;$v28 311 $a3-319-78731-4 327 $aIntroduction -- Classical Markov chains -- Quantum Markov chains -- Outline -- Preliminaries -- Notation -- Schatten norms -- Functions on Hermitian operators -- Quantum channels -- Entropy measures -- Background and further reading -- Tools for non-commuting operators -- Pinching -- Complex interpolation theory -- Background and further reading -- Multivariate trace inequalities -- Motivation -- Multivariate Araki-Lieb-Thirring inequality -- Multivariate Golden-Thompson inequality -- Multivariate logarithmic trace inequality -- Background and further reading -- Approximate quantum Markov chains -- Quantum Markov chains -- Sufficient criterion for approximate recoverability -- Necessary criterion for approximate recoverability -- Strengthened entropy inequalities -- Background and further reading -- A A large conditional mutual information does not imply bad recovery -- B Example showing the optimality of the Lmax-term -- C Solutions to exercises -- References -- Index. 330 $aThis book is an introduction to quantum Markov chains and explains how this concept is connected to the question of how well a lost quantum mechanical system can be recovered from a correlated subsystem. To achieve this goal, we strengthen the data-processing inequality such that it reveals a statement about the reconstruction of lost information. The main difficulty in order to understand the behavior of quantum Markov chains arises from the fact that quantum mechanical operators do not commute in general. As a result we start by explaining two techniques of how to deal with non-commuting matrices: the spectral pinching method and complex interpolation theory. Once the reader is familiar with these techniques a novel inequality is presented that extends the celebrated Golden-Thompson inequality to arbitrarily many matrices. This inequality is the key ingredient in understanding approximate quantum Markov chains and it answers a question from matrix analysis that was open since 1973, i.e., if Lieb's triple matrix inequality can be extended to more than three matrices. Finally, we carefully discuss the properties of approximate quantum Markov chains and their implications. The book is aimed to graduate students who want to learn about approximate quantum Markov chains as well as more experienced scientists who want to enter this field. Mathematical majority is necessary, but no prior knowledge of quantum mechanics is required. 410 0$aSpringerBriefs in Mathematical Physics,$x2197-1757 ;$v28 606 $aQuantum theory 606 $aMathematical physics 606 $aCondensed matter 606 $aStatistical physics 606 $aQuantum computers 606 $aSpintronics 606 $aQuantum Physics$3https://scigraph.springernature.com/ontologies/product-market-codes/P19080 606 $aMathematical Physics$3https://scigraph.springernature.com/ontologies/product-market-codes/M35000 606 $aCondensed Matter Physics$3https://scigraph.springernature.com/ontologies/product-market-codes/P25005 606 $aStatistical Physics and Dynamical Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/P19090 606 $aQuantum Information Technology, Spintronics$3https://scigraph.springernature.com/ontologies/product-market-codes/P31070 615 0$aQuantum theory. 615 0$aMathematical physics. 615 0$aCondensed matter. 615 0$aStatistical physics. 615 0$aQuantum computers. 615 0$aSpintronics. 615 14$aQuantum Physics. 615 24$aMathematical Physics. 615 24$aCondensed Matter Physics. 615 24$aStatistical Physics and Dynamical Systems. 615 24$aQuantum Information Technology, Spintronics. 676 $a519.233 700 $aSutter$b David$4aut$4http://id.loc.gov/vocabulary/relators/aut$0833856 906 $aBOOK 912 $a9910300533903321 996 $aApproximate Quantum Markov Chains$91864306 997 $aUNINA