Assessing Random Dynamical Network Architectures for Nanoelectronics

Assessing Random Dynamical Network Architectures for Nanoelectronics

Christof Teuscher 1,Natali Gulbahce 2,Thimo Rohlf 3

1Computer,Computational &Statistical Sciences Division,Los Alamos National Laboratory,USA,

christof@teuscher.ch

2Center for Complex Networks Research,Northeastern University,USA,

natali.gulbahce@https://www.wendangku.net/doc/0f13179345.html,

3Max Planck Institute for Mathematics in the Sciences,Leipzig,Germany,

rohlf@https://www.wendangku.net/doc/0f13179345.html,

Abstract —Independent of the technology,it is generally ex-pected that future nanoscale devices will be built from vast numbers of densely arranged devices that exhibit high failure rates.Other than that,there is little consensus on what type of technology and computing architecture holds most promises to go far beyond today’s top-down engineered silicon devices.Cellular automata (CA)have been proposed in the past as a possible class of architectures to the von Neumann computing architecture,which is not generally well suited for future mas-sively parallel and ?ne-grained nanoscale electronics.While the top-down engineered semi-conducting technology favors regular and locally interconnected structures,future bottom-up self-assembled devices tend to have irregular structures because of the current lack of precise control over these processes.In this paper,we will assess random dynamical networks,namely Random Boolean Networks (RBNs)and Random Threshold Networks (RTNs),as alternative computing architectures and models for future information processing devices.We will illustrate that—from a theoretical perspective—they offer superior properties over classical CA-based architectures,such as inherent robustness as the system scales up,more ef?cient information processing capabilities,and manufacturing bene?ts for bottom-up designed devices,which motivates this investigation.We will present recent results on the dynamic behavior and robustness of such random dynamical networks while also including manufacturing issues in the assessment.

I.I NTRODUCTION AND M OTIVATION

The advent of multicore architectures and the slowdown of the processor’s operating frequency increase are signs that CMOS miniaturization is increasingly hitting fundamental physical limits.A key question is how computing architectures will evolve as we reach these fundamental limits.A likely possibility within the realm of CMOS technology is that the integration density will cease to increase at some point,instead only the number of components,i.e,the transistors,will further increase,which will necessarily lead to chips with a higher area.This trend can already be observed with multi-core ar-chitectures.That in itself has implications on the interconnect architecture,the power consumption and dissipation,and the reliability.Another possibility is to go beyond silicon-based technology and to change the computing and manufacturing paradigms,by using for example bottom-up self-assembled devices.Self-assembling nanowires [12]or carbon nanotube electronics [2]are promising candidates,although none of

them has resulted in electronics that is able to compete with traditional CMOS so far.What seems clear is that the current way with build computers and the way we algorithmically solve problems with them may need to be fundamentally revisited,which this paper is all about.

While the top-down engineered CMOS technology favors regular and locally interconnected structures,future bottom-up self-assembled devices tend to have irregular structures because of the current lack of precise control over these processes.We therefore hypothesize that future and emerging computing architectures will be much more driven by manu-facturing constraints and particularities than for CMOS,which allowed engineers to implement a logic-based computing ar-chitecture with extreme precision and reliability,at least in the past.Independent of the forthcoming device and fabrication technologies,it is generally expected that future nanoscale devices will be built from (1)vast numbers of densely arranged devices that (2)exhibit high failure rates.We take this working hypothesis for granted in this paper and address it from a perspective that focuses on the interconnect topology.This is justi?ed by the fact that the importance of interconnects on electronic chips has outrun the importance of transistors as a dominant factor of performance [9],[15],[25].The reasons are twofold:(1)the transistor switching speed for traditional silicon is much faster than the average wire delays and (2)the required chip area for interconnects has dramatically increased.In [45],Zhirnov et al.explored integrated digital Cellu-lar Automata (CA)architectures—which are highly regular structures with local interconnects (see Section III)—as an alternative paradigms to the von Neumann computer architec-ture for future and emerging information processing devices.Here,we are interested to explore and assess a more general class of discrete dynamical systems,namely Random Boolean Networks (RBNs)and Random Threshold Networks (RTNs).We will mainly focus on RBNs,but RTNs are included in this paper because they offer an alternative paradigm to Boolean logic,which can be ef?ciently implemented as well (see Section VII).

Motivated by future and emerging nanoscale devices,we are interested to provide answers to the following questions:?Do RBNs and RTNs offer bene?ts over CA-architectures?

a r X i v :0805.2684v 1 [c s .A R ] 17 M a y 2008

If yes,what are they?

?How does the interconnect complexity compare between RBNs/RTNs and CAs?

?Does any of these architectures allow to solve problems more ef?ciently?

?Is any of these architectures inherently more robust to simple errors?

?Can CMOS and beyond-CMOS devices provide a bene?t for the fabrication of any of these architectures?

We will argue and illustrate that—at least from a theoret-ical perspective—random dynamical networks offer superior properties over classical regular CA-based architectures,such as inherent robustness as the system scales up,more ef?cient information processing capabilities,and manufacturing ben-e?ts for bottom-up fabricated devices,which motivates this investigation.We will present recent results on the dynamic behavior and robustness of such random dynamical networks while also including manufacturing issues in the assessment. To answer the above questions,we will extend recent results on the complex dynamical behavior of discrete random dynamical networks[34],their ability to solve problems[26], [39],and novel interconnect paradigms[37],[38].

The remainder of this paper is as following:Section

II introduces random dynamical networks,namely random Boolean and random threshold networks.Section III brie?y presents cellular automata architectures.Damage spreading and criticality of cellular automata and random dynamical networks is analyzed in Section IV.Section V analyzes the network topologies from a graph-theoretical and wiring-cost perspective.The task solving capabilities of RBNs and CAs are brie?y assessed in Section VI,while Section VII looks into manufacturing issues.Section VIII concludes the paper.

II.R ANDOM D YNAMICAL N ETWORKS

A.Random Boolean Networks

A Random Boolean Network(RBN)[18]–[20]is a discrete dynamical system composed of N nodes,also called automata, elements or cells.Each automaton is a Boolean variable with two possible states:{0,1},and the dynamics is such that

F:{0,1}N→{0,1}N,(1) where F=(f1,...,f i,...,f N),and each f i is represented by a look-up table of K i inputs randomly chosen from the set of N nodes.Initially,K i neighbors and a look-table are assigned to each node at random.Note that K i(i.e.,the fan-in) can refer to the exact or to the average number of incoming connections per node.

A node stateσt i∈{0,1}is updated using its corresponding Boolean function:

σt+1 i =f i(x t i

1

,x t i

2

,...,x t i

K i

).(2)

These Boolean functions are commonly represented by lookup-tables(LUTs),which associate a1-bit output(the node’s future state)to each possible K-bit input con?guration. The table’s out-column is called the rule of the node.Note that even though the LUTs of a RBN map well on an FPGA or other memory-based architectures,the random interconnect in general does not.

We randomly initialize the states of the nodes(initial condition of the RBN).The nodes are updated synchronously using their corresponding Boolean functions.Other updating schemes exist,see for example[13]for an overview.Syn-chronous random Boolean networks as introduced by Kauff-man are commonly called NK networks or models.Figure1 shows a possible NK random Boolean network representation (N=8,K=3).

Fig.1.Illustration of a random Boolean network with N=8nodes and K=3inputs per node(self-connections are allowed).The node rules are commonly represented by lookup-tables(LUTs),which associate a1-bit output(the node’s future state)to each possible K-bit input con?guration. The table’s out-column is commonly called the rule of the node.

B.Random Threshold Networks

Random Threshold Networks(RTNs)are another type of discrete dynamical systems.An RTN consists of N randomly interconnected binary sites(spins)with statesσi=±1.For each site i,its state at time t+1is a function of the inputs it receives from other spins at time t:

σi(t+1)=sgn(f i(t))(3) with

f i(t)=

N

j=1

c ijσj(t)+h.(4)

The N network sites are updated synchronously.In the following,the threshold parameter h is set to zero.The interaction weights c ij take discrete values c ij=+1or?1 with equal probability.If i does not receive signals from j, one has c ij=0.

III.C ELLULAR A UTOMATA A RCHITECTURES Cellular automata(CA)[44]were originally conceived by Ulam and von Neumann[41]in the1940s to provide a formal framework for investigating the behavior of complex, extended systems.CAs are a special case of the more general class of random dynamical networks,in which space and time are discrete.A CA usually consists of a D-dimensional

regular lattice of N lattice sites,commonly called nodes ,cells ,elements ,or automata .Each cell i can be in one of a ?nite number of S possible states and further consists of a transition function f i (also called rule ),which maps the neighboring states to the set of cell states.CAs are called uniform if all cells contain the same rule,otherwise they are non-uniform .Each cell takes as input the states of the cells within some ?nite local neighborhood.Here,we only consider non-uniform,two-dimensional (D =2),folded,and binary CAs (S =2)with a radius-1von Neumann neighborhood,where each cell is

connected to each of its four immediate neighbors only.Figure 2illustrates such an CA.The Boolean functions in each node must therefore de?ne 24=16possible input combinations.To be able to compare CAs with RBNs,we do not consider self-connections.

LSB

MSB

Fig.2.Illustration of a binary,2D,folded cellular automaton with N =16cells.Each node is connected to its four immediate neighbors (von Neumann neighborhood).

IV.D AMAGE S PREADING AND C RITICALITY

A.Random Boolean and Threshold Networks

As we have seen in Section II-B,RBNs and their complex dynamic behavior are essentially characterized by the average number of incoming links K i (fan-in)per node (e.g.,Figure 1shows a K =3network with 3incoming links per node).It turns out that in the thermodynamic limit,i.e.,N →∞,RBNs exhibit a dynamical order-disorder transition at a sparse critical connectivity K c =2[10](i.e.,where each node receives on average two incoming connections from two randomly chosen other nodes),which partitions their operating space into 3different regimes:(1),sub-critical,where K K c .In the sub-critical regime,the network dynamics are too “rigid”and the information processing capabilities are thus hindered,whereas in the supercritical regime,their behav-ior becomes chaotic.The complex regime is also commonly called the “edge of chaos,”because it represents the network connectivity where information processing is “optimal”and where a small number of stable attractors exist.

Similar observations were made for sparsely connected ran-dom threshold (neural)networks (RTN)[33]for K c =1.849.For a ?nite system size N ,the dynamics of both systems converge to periodic attractors after a ?nite number of updates.At K c ,the phase space structure in terms of attractor periods [1],the number of different attractors [35]and the distribution

d ()

d ()

RBN

RTN

1e?04

0.001 0.01

0.1 1

10 100

0 0.5 1 1.5 2 2.5 3

N=32N=64N=128N=256N=512

1e?04

0.001 0.01 0.1 1 10 100 0

0.5

1

1.5

2

2.5

3

N=32

N=64N=128N=256N=512N=1024

Fig.3.Average Hamming distance (damage) d after 200system updates,averaged over 10000randomly generated networks for each value of K ,with 100different random initial conditions and one-bit perturbed neighbor con?gurations for each network.For both RBN and RTN,all curves for different N approximately intersect in a characteristic point K s .

of basins of attraction [3]is complex,showing many properties reminiscent of biological networks [20].

Results:In [34]we have systematically studied and compared damage spreading (i.e.,how a perturbed node-state in?uences the rest of the network nodes over time)at the sparse percolation (SP)limit for random Boolean and threshold networks with perturbations.In the SP limit,the damage induced in a network (i.e.,by changing the state of a node)does not scale with system size.Obviously,this limit is relevant to information and damage propagation in many tech-nological and natural networks,such as the Internet,disease spreading in populations,failure propagation in power grids,and networks-on-chips.We measure the damage spreading by the following methodology:the state of one randomly chosen node is changed.The damage is measured as the Hamming distance between a damaged and undamaged network instance after a large number of T system updates.

We have shown that there is a characteristic average con-nectivity K RBN s =1.875for RBNs and K RT N

s =1.729for RTNs,where the damage spreading of a single one-bit perturbation of a network node remains constant as the system size N scales up.Figure 3illustrates this newly discovered point for RBNs and RTNs.For more details,see [34].

Discussion:Both K c and K s are highly relevant for nano-scale electronics for the following reason:assuming we can build massive numbers of N simple logic gates that implement a random Boolean function,the above ?ndings tell us that on average,every gate should be connected somewhere close to both K s and K c in order to (1)guarantee optimal robustness against failures for any system size and (2)optimal information processing at the “edge of chaos.”We are also hypothesizing that natural systems,such as the brain or genetic regulatory networks,may have evolved towards these characteristic connectivities.This remains,however,to be proved and is part of ongoing research.B.Cellular Automata Damage Spreading

We have used the same approach as described above to measure the damage spreading in cellular automata.In order to vary the average number K of incoming links per cell in a cellular automata (e.g.,as pictured in Figure 2),we have adopted the following methodology:(1)for a desired average

number of links per cell K for a given CA size of N cells,the total number of links in the automaton is given by L =N K ;(2)we then randomly choose L possible connections on the regular CA-grid with uniform probability and establish the links.Damage is induced in the same way as for RBNs and RTNs:the state of one (or several)randomly chosen node(s)is changed.The damage is measured as the Hamming distance between a damaged and undamaged CA instance after a large number of T system updates,in our case T =200.

Results:Figures

4,5,and 6show the average damage of both RBNs and CAs for different system sizes and for a damage size of 1and 10respectively.We have left out RTNs for this analysis.As one can see,both the RBN and the CA average damage for different N approximately intersect in

the characteristic point K RBN

s =1.875.This point is less pronounced for the larger damage sizes (Figures 5and 6).The RBN curves con?rm what was already shown above in Figure 3,and are merely plotted here for comparison with the CA architectures and their system sizes imposed by square lattices.

Interestingly,the CAs show different damage propagation behavior for different system sizes and connectivities.First,we observe that the average damage for one-bit damage events (Figure 4)is independent of the system size N for up to approximatively K =2.5average incoming connections per cell.This behavior disappears completely for large damage sizes (Figure 6).Second,Figure 4shows that all curves

intersect at K RBN

s =K CA s

=1.875.Third,Figure 6suggest that for larger damage sizes,K CA

s disappears for CAs.Fourth,the average damage for larger damage events,i.e.,10and 20in our examples,converges to the same ?nal values for both RBNs and CAs as K approaches 4.

Discussion:We hypothesize that the particular behavior can be explained by the percolation limit of the cellular automata.Da Silva et al.[8]found that the link probability at the percolation limit is approximatively p ～0.6,which means that the average connectivity at the percolation limit in our CA topology with a maximum of 4neighbors is given by k =4p =2.4.This value corresponds to the experimentally observed value where the damage spreading suddenly becomes dependent of the system size.Because of the local CA connectivity,there are lots of disconnected components below the percolation limit.Below this limit,the damage spreading is thus very slow and limited by the disconnected components,reason why it is essentially independent of system size.Above the percolation limit,the CA suddenly becomes connected and damage spreading becomes therefore dependent on the system size.For larger damage events,such as 10or 20,damage becomes more dependent on system size even below the percolation limit because there is a higher probability that damage is induced in several disconnected components at the same time.

In summary:for single-node damage events,CAs offer system-size independent damage spreading for up to about K =2.4(which corresponds to the percolation limit),however,this particular behavior disappears for larger damage

d ()

Fig.4.Average Hamming distance (damage) d after 200system updates,averaged over 100randomly generated networks for each value of K ,with 100different random initial conditions and a damage size of 1node for each network.See text for discussion.

d ()

Fig.5.Average Hamming distance (damage) d after 200system updates,averaged over 100randomly generated networks for each value of K ,with 100different random initial conditions and a damage size of 10nodes for each network.See text for discussion.

events.We conclude that in the general case,CAs do not pos-sess a characteristic connectivity K s ,where damage spreading is independent of the system size N .Such a connectivity,however,exists for both RBNs and RTNs,which makes them particularly suitable as a computing model in an environment with high error probabilities or systems with low system component reliabilities.An example are logical gates based on bio-molecular components [4],where high failure rates can be expected.

V.C OMPLEX N ETWORKS AND W IRING C OSTS

Most real networks,such as brain networks [11],[36],electronic circuits [17],the Internet,and social networks share the so-called small-world (SW)property [43].Compared to purely locally and regularly interconnected networks (such as for example the CA interconnect of Figure 2),small-world networks have a very short average distance (measured as the number of edges to traverse)between any pair of

d ()

Fig.6.Average Hamming distance (damage) d after 200system updates,averaged over 100randomly generated networks for each value of K ,with 100different random initial conditions and a damage size of 20nodes for each network.See text for discussion.

nodes,which makes them particularly interesting for ef?cient communication.

The classical Watts-Strogatz small-world network [43]is built from a regular lattice with only nearest neighbor connec-tions.Every link is then rewired with a rewiring probability p to a randomly chosen node.Thus,by varying p ,one can obtain a fully regular (p =0)and a fully random (p =1)network topology.The rewiring procedure establishes “shortcuts”in the network,which signi?cantly lower the average distance (i.e.,the number of edges to traverse)between any pair of nodes.In the original model,the length distribution of the shortcuts is uniform since a node is chosen randomly.If the rewiring of the connections is done proportional to a power law,l ?α,where l is the wire length,then we obtain a small-world power-law network .The exponent αaffects the network’s communication characteristics [23]and navigability [21],which is better than in the uniformly generated small-world network.One can think of other distance-proportional distributions for the rewiring,such as for example a Gaussian distribution,which has been found between certain layers of the rat’s neocortical pyramidal neurons [14].

In a real network,it is fair to assume that local connections have a lower cost (in terms of the associated wire-delay and the area required)than long-distance connections.Physically re-alizing small-world networks with uniformly distributed long-distance connections is thus not realistic and distance,i.e.,the wiring cost,needs to be taken into account,a perspective that recently gained increasing attention [30].On the other hand,a network’s topology also directly affects how ef?cient problems can be solved.

Teuscher [37]has pragmatically and experimentally in-vestigated important design trade-offs and properties of an irregular,abstract,yet physically plausible 3D small-world interconnect fabric that is inspired by modern network-on-chip paradigms.The results con?rm that (1)computation in irregular assemblies is a promising and disruptive comput-

ing paradigm for self-assembled nano-scale electronics and (2)that 3D small-world interconnect fabrics with a power-law decaying distribution of shortcut lengths are physically plausible and have major advantages over local 2D and 3D regular topologies,such as CA interconnects.

Discussion:There is a trade-off between (1)the physical realizability and (2)the communication characteristics for a network topology.A locally and regularly interconnected topology,such as that of a CA,is in general easy to build (especially for to-down engineered CMOS technology)and only involves minimal wire and area cost (as for example shown by Zhirnov et al.[45]),but it offers poor global commu-nication characteristics and scales-up poorly with system size.On the other hand,a random topology,such as that of RBNs or RTNs,scales-up well and has a very short-average path length,but it is not physically plausible because it involves costly long-distance connections established independently of the Euclidean distance between the nodes.The RBN and RTN topologies we consider here as thus extremes,such as CA topologies,the ideal lies in between:small-world topologies with a distance-dependent distribution of the connectivity.Such topologies are located in a unique spot in the design space and also offer two other highly relevant properties [22],[37]:(1)ef?cient navigability and thus potentially ef?cient routing,and (2)robustness against random link removals.For these reasons,we can conclude that small-world graphs are the most promising interconnects for future massive scale devices.

VI.A G LANCE ON T ASK S OLVING

In [26],Mesot and Teuscher have presented a novel an-alytical approach to ?nd the local rules of random Boolean networks to solve the global density classi?cation and the synchronization task—which are well known benchmark tasks in the CA community—from any initial con?guration.They have also quantitatively and qualitatively compared the results with previously published work on cellular automata and have shown that randomly interconnected automata are computa-tionally more ef?cient in solving these two global tasks.

In addition,preliminary results by the authors [39]also suggest that K c =2RBN generalize better on simple learning tasks than sub-critical or supercritical networks,but more research will be necessary.

Discussion:To ef?ciently solve algorithmic problems with distributed computing architectures,ef?cient communi-cation is key.This is particularly true for tasks such as the density or the synchronization task,which are trivial to solve if one has a global view on the entire system state,but non-trivial to solve if each cell only sees a limited number of neighboring cells.It is thus not surprising that cells interconnected by a network with the small-world property perform much better on such tasks because the information propagation is signi?cantly better.This is a too often neglected fact for CAs,in particular if one wants to use them as a viable mainstream and general purpose computing architecture.It is well-know that even simple CAs are computationally universal (and so are RBNs),i.e.,they can solve any algorithmic problem,but due to their

local non-small-world interconnect topology,that will only be possible in a highly inef?cient way in the general case,i.e.,for a large set of different applications.This is well illustrated with the(highly inef?cient)implementation of a universal Turing machine on top of the Game of Life[32].Naturally,there are exceptions to the general case,and it has been shown that CAs can be extremely ef?cient for certain niche applications,such as for examle image processing.

VII.M ANUFACTURING I SSUES

As Chen et al.[6]state,“[i]n order to realize functional nano-electronic circuits,researchers need to solve three prob-lems:invent a nanoscale device that switches an electric current on and off;build a nanoscale circuit that controllably links very large numbers of these devices with each other and with external systems in order to perform memory and/or logic functions;and design an architecture that allows the circuits to communicate with other systems and operate independently on their lower-level details.”

While we can currently build switching devices in vari-ous technologies besides CMOS(see[5],[16],[47]for an overview),one of the remaining challenges is to assemble and interconnect these switching devices(or logic functions) to larger systems,and ultimately to design a computing architecture that allows to perform reliable computations.As mentioned before,there is little consensus in the research com-munity on what type of technology and computing architecture holds most promises for the future.

The motivation for investigating randomly assembled inter-connects and computing architectures can be summarized by the following observations:

?long-range and global connections are costly(in terms of wire delay and of the chip area used)and limit system performance[15];

?it is unclear whether a precisely regular and homogeneous arrangement of components is needed and possible on a multi-billion-component or even Avogadro-scale assem-bly of nano-scale components[40]

?“[s]elf-assembly makes it relatively easy to form a ran-dom array of wires with randomly attached switches”

[46];and

?building a perfect system is very hard and expensive We have hypothesized in[38]and[37]that bottom-up self-assembled electronics based on conductive nanowires or nanotubes can lead to the random interconnect topologies we are interested in,however,several questions remain open and are part of a3-year interdisciplinary research project at LANL. Our approach consists in using a hybrid assembly(as others explore as well, e.g.,[12]),where the functional building blocks will still be traditional silicon in a?rst step,while the interconnect is made up from self-assembled nanowires. Nanowires can be grown in various ways using diverse mate-rials,such as metals and semiconductors.We have chosen a novel way to grow conductive nanowires,which Wang et al.

[42]at LANL have pioneered and demonstrated:Ag nanowires can be fabricated on top of conducting polyaniline polymer membranes via a spontaneous electrodeless deposition(self-

assembly)method.We hypothesize that this will allow to

densely interconnect silicon components in a simple and cheap

way with speci?c distance-dependent wire-length distributions.

We believe that this approach will ultimately allow us to easily

and cheaply fabricate RBN-like computing architectures.

Random threshold networks,on the other hand,could be

rather straightforwardly and ef?ciently implemented with res-

onant tunneling diode(RTD)logic circuits(see e.g.,[31]),

and represent a very interesting alternative to conventional

Boolean logic gates.The reported results in this paper on

random threshold networks can thus directly be applied to the

implementation of such devices.There has been a signi?cant

body of research in the area of threshold logic in the past(see

e.g.,[27]),but to the best of our knowledge,random threshold

networks have not been considered as computing models for

future and emerging computing machines.

VIII.C ONCLUSION

The central claim of this paper is that locally interconnected

computing architectures,such as cellular automata(CA),are

in general not appropriate models for large-scale and general-

purpose computations.We have supported this claim with re-

cent theoretical results on the complex dynamical behavior of

discrete random dynamical networks,their robustness to dam-

age events as the system scales up,their ability to ef?ciently

solve tasks,and their improved transport characteristics due

to the short average path length.The arguments,in a nutshell,

why we believe that CAs are not promising architectures for

future information-processing devices,are as following:?their local interconnect topology is not small-world and has thus worse global transport characteristics(than

small-world or random graphs),which directly affects the

effectiveness of how general-purpose algorithmic tasks

can be solved;

?in terms of a complex dynamical system,they operate in the supercritical regime( K >K c)with the widely used von Neumann neighborhood,which makes them sensitive to initial conditions;

?they do not generally have a characteristic connectivity K s,where damage spreading is independent of system size,which makes a system inherently robust;and

?it is unclear whether a precisely regular and homogeneous arrangement of components is possible at the scale of future information processing devices.

We have assessed RBNs and RTNs as alternative models,

however,as we have seen in Section V they come at a serious

cost:the uniform probability to establish connections with

any node in the system independent of the Euclidean distance

between them is not physically plausible and too expensive

in terms of wiring cost.The ultimate interconnect topology

is small-world and has a distance-dependent distribution of

the wires[30],[37],[38].We have preliminary evidence that,

if we were to connect RBNs and RTNs by such a network

topology,both K c and K s would still exist.Research to clarify

this question is under progress,

Open Questions and Unaddressed Issues:Naturally,there are a number of open questions and issues that we have not addressed because they are beyond the scope of this paper.In particular,an irregular topology with random logical functions makes the mapping of a given digital circuit much harder,if not impossible in certain cases.On the other hand,a regular interconnect topology clearly makes the mapping task easier. We believe,however,that this challenge can be addressed by automated design tools.After all,computation in random assemblies is not completely new and has been more or less successfully tried by others,e.g.,[24],[28],[40],however in different contexts and with a different perspective in mind than we have presented here.

We have deliberately not focused on any particular ap-plication in this paper because our results are independent of the application.However,it is noteworthy that locally interconnected CAs have been proven to outperform other general purpose architecture on very speci?c applications.A good example are cellular neural networks(CNNs)[7],which, e.g.,allow to perform certain imagine processing tasks orders of magnitude faster than any other machine.

Further,it is unknown at this point how exactly our?ndings ?t into the interconnect predictions made by Rent’s rule, however,the rule may not be applicable to our non-traditional circuits since it is based on empirical results.Further research on this is planned.

Last but not least,we would like to mention that,although we have only considered2D arrangements and interconnects here for simplicity,the future is clearly3D(e.g.,see[29]).The main reason is that the average wire length in3D is shorter than in2D interconnects.

Outlook:We believe that computation in random self-assemblies of simple components and interconnections is a highly appealing paradigm,both from the perspective of fabrication as well as performance and robustness.Future work will focus on(1)the manufacturing issues,(2)appropriate design methodologies,(3)addressing the mapping issues,and (4)more realistic models,which will allow to better assess the performance and cost,and(5)speci?c applications. Acknowledgments

We gratefully acknowledge the support of the U.S.Depart-ment of Energy through the LANL/LDRD Program for this work.The authors would like to thank Elshan A.Akhadov and Hsing-Lin Wang.

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五款最好的免费电脑资料同步备份软件

文件夹同步就是将两个文件夹内的文件内容进行分析，可选择性的让两个文件夹内容保存一直。文件夹同步软件相当有用，虽然大多数人没用过，但它确实能够为你节省很多时间和操作。比如说：同步U盘上的数据和软件设置，查找软件版本区别和更新，同步FTP上的数据。我认为，很多情况下使用同步软件可以极大提高计算机操作效率。 高效文件同步工具GoodSync 在多种驱动设备之间自动同步和备份，（个人电脑、移动设备、网络设备）支持任何文件类型，支持多任务、多语言。人性化的界面，可自由选择部分单向双向同步，有强大的过滤系统，有完整的日志记录及更改内容报表。 注意：GoodSync分析之后会在任务文件夹生成“_gsdata_”的隐藏文件夹，里面存放在任务日志和备份文件。GoodSync有免费版和专业版之分。免费版在30天内没有任何限制，仅仅是不能可用于商业用途和政府机构。过来三十天依然可以免费使用，但是仅支持3个任务（相比很多单任务的还是强大不少）和每次100文件夹的同步工作（一般情况下够）。下载 开源同步软件FreeFileSync 界面简洁，操作简单。虽然是单任务，但是可以保存和加载配置。最重要的是，作为一款开源如软件，它没有任何限制。下载

多文件夹同步器Allway Sync Allway Sync 是一个非常容易使用的 Windows 文件同步软件。同样支持在多种设备进行同步、多向同步（1个文件夹到N个）、自动同步。有极其强大的过滤规则、错误管理，可以压缩备份、加密备份。可导出导入xml格式配置文件和任务。免费版有文件大小和数量限制。当然，有着强大功能的同时，体积和资源占用也偏大。下载

汇博通文档借阅管理组织系统软件使用使用说明

汇博通文档借阅管理系统使用说明书 汇博通知识管理系统的属性管理，实际上已提供了借阅与归还功能，但那是针对每一份文件 或档案而言的。 这里，为客户提供一款专门用于文档的借阅与归还的软件，不但可办理一份文件的借阅或归 还手续，只要有需要，也可批量办理借阅与归还，另外，还提供了与借阅有关的一系列统计 报表。 发放功能与借阅类似，所不同的只是发放不必归还，如将购买的资料、图书发放给职员学习 等。 注：借阅与归还模块的操作，需要获得以下三种权限中的一种： 系统管理员 归档授权（档案管理员） 编号授权（文件管理员） 与借阅与归还模块相关的系统参数的设置说明如下：

首页 汇博通主页的模块工具条上，有一个借阅与归还的按钮，单击它即进入借阅与归还首页。 借阅（发放） 前面已经介绍过，借阅与发放的区别在于，借阅需要归还，发放则不必归还，从某种意义上 来说，发放实际上已将所有权（或有条件的所有权）转移给接收者。 借阅界面包括左右两个子窗体，左侧子窗体用于显示可供借阅（发放）的文档，其上部有搜 索关键词输入框，输入相应关键词即可查找出可供借阅的相应文档，如果要借阅的文档已经 在操作者手上，并且，标注有条形码或电子标签，操作者可直接通过条码阅读器或电子标签

阅读器读取相应编码直接获取到该文档。 根据实际需要，通过点选左侧的复选框，选择具体文档，然后，通过点击两个子窗体中间的箭头，即可将选中的文档添加到右侧子窗体的列表中，即可直接办理借阅或发放手续。 可供借阅（发放）检索列表待选区。借阅（发放）选择勾选列表区。 可供借阅（发放） 输入文件名称、编号、责任者或主题词等属性，点击【检索】按钮进行查找，如下图： 勾选确定后点击该按钮，即可添加到已 选择列表区中。

如何使用群晖备份、同步文件

如何使用群晖备份、同步文件？通过群晖管家安装好NAS之后，想要实现备份、同步还要随时随地查看所有的文件？只需要一个Drive，就能把你的需求统统搞定。让你轻松的掌握文件同步和备份。 Drive既是备份盘、同步盘、网盘，还可以是协作盘。集中管理所有文件，还能够同步不同电脑上的数据。团队脑风暴时，可以多人在同一个文档上实时协同编辑，还能够备份电脑上的文件并且提供多版本保护。 安装及设置drive套件 1、打开群晖DSM界面，在套件中心安装Drive套件。 2、安装Drive套件会一并安装Drive管理控制台——顾名思义，就是可以设置Drive 相关功能、管理所有备份和同步的设备、查看历史版本等。 3、建议你在Drive管理控制台启用深度搜索，就可以在Drive里面查找内文关键字还有照片各种原始信息，步骤如图。

设置备份盘 1、首先进入群晖官网的下载中心，根据NAS机型，选择下载“Drive Client”PC客户端，Windows、Mac、Ubuntu一应俱全，系统兼容妥妥的。 2、PC客户端安装完成后，根据需求修改Drive服务器（NAS端）和电脑（本地端）的不同文件夹。 3、设置完成后，进入Drive PC客户端的控制面板，将同步模式改成“单向上传”，点击应用，然后就开始备份啦。 同步盘如何实现 1、同步盘很简单，设置步骤跟上面的备份盘一样，在需要同步的电脑上安装Drive PC客户端，并且选择双向同步。 2、如果在办公场景，希望把他人分享给你的共享文件夹同步到电脑本地，在PC客户 端控制面板点击“创建>启用同步与我共享”，这么一来，别人与你分享的文件也会同步到本地。

人力资源管理系统软件操作手册

XX集团—人力资源管理系统操作手册 目录 常用操作（新人必读） (2) 1.基础数据管理 ................................................................................................................... - 5 - 1.1组织架构 (5) 1.2职位体系 (8) 1.3职员维护 (11) 1.4结束初始化.................................................................................. 错误！未定义书签。 2.组织管理业务 ................................................................................................................. - 27 - 2.1组织规划 (27) 2.2人力规划 (33) 2.3组织报表 (38) 3.员工管理业务 ................................................................................................................. - 41 - 3.1员工状态管理 (41) 3.2合同管理 (41) 3.3后备人才管理 .............................................................................. 错误！未定义书签。 3.4人事事务 (52) 3.5人事报表 (59) 4.薪酬管理 ......................................................................................................................... - 69 - 4.1基础数据准备 (69) 4.2薪酬管理日常业务 (92) 4.3薪酬管理期末业务 (107) 4.4薪酬报表 (108)

销售管理软件操作手册

前言 本《操作手册》内容是按该软件主界面上第一横排从左至右的顺序对各个功能加以介绍的，建议初学者先对第一章系统设置作初步了解，从第二章基础资料读起，回头再读第一章。该管理软件的重点与难点是第二章，望读者详读。 第一章系统设置 打开此管理软件，在主界面上的左上方第一栏就是【系统设置】，如下图所示： 点击【系统设置】，在系统设置下方会显示【系统设置】的内容，包括操作员管理、数据初始化、修改我的登录密码、切换用户、选项设置、单据报表设置、导入数据、数据库备份、数据库恢复、压缩和修复数据库、退出程序。下面分别将这些功能作简要介绍： 1.1操作员管理 新建、删除使用本软件的操作员，授权他们可以使用哪些功能。此功能只有系统管理员可以使用。 1.1.1 进入界面 单击【系统设置】，选择其中的【操作员管理】，画面如下：

1.1.2、增加操作员 单击【新建】按钮，画面如下： 输入用户名称、初始密码、选择用户权限，可对用户进行适当描述，按【保存】后就点【退出】，就完成了新操作员的添加，效果如下图。

1.1.3 删除操作员 选择要删除的操作员，单击【删除】按钮。 1.1.4 修改操作员 选择要修改的操作员，单击【修改】按钮，可对操作员作相应修改，修改后需保存。 1.1.5 用户操作权限 选择要修改的操作员，单击【修改】按钮，出现以下画面，点击【用户权限】栏下的编辑框，出现对号后点【保存】，该操作员就有了此权限。 1.2数据初始化 1.2.1进入界面 单击【系统设置】，选择其中的【数据初始化】，画面如下：

1.2.2数据清除 选择要清除的数据，即数据前出现对号，按【确定】后点【退出】，就可清除相应数据。 1.3 修改我的登录密码 1.3.1进入界面 单击【系统设置】，选择其中的【修改我的登录密码】，画面如下： 1.3.2密码修改 输入原密码、现密码，然后对新密码进行验证，按【确定】后关闭此窗口，就可完成密码修改。 1.4 切换用户 1.4.1进入界面 单击【系统设置】，选择其中的【切换用户】，画面如下：

多文件夹的自动同步和各向同步工具

多文件夹的自动同步和各向同步工具 出处：小建の软件园作者：佚名日期：2008-06-25 关键字：同步 对于经常需要备份文件，同步文件的网友，Allway Sync 可谓不可多得，虽然不能激活其专业版，对文件数量多和经常性的同步操作可能会超过免费版的限制，不过对于一般文件数量不多同步操作可以完全满足，Allway Sync 使用相当简单，多种同步方式能满足你不同需求。对重要文件进行备份是文件恢复最好的方法，而 Allway Sync 可以简化你许多备份的过程，能实现自动备份，如果你“胃口”不大，免费版应当已经可以满足。 下载地址：https://www.wendangku.net/doc/0f13179345.html,/soft/23495.html Allway Sync 可以进行自动同步，可以对的文件/文件夹进行筛选，只备份需要的东东。

Allway Sync 备份方式介绍 - 同步方式有源文件夹同步和各向同步两种方式： 1、源文件夹同步方式将以一个文件夹为基准，删除或覆盖其余文件夹与源文件相比较不相同的文件。 2、各向同步方式则自动将更新的文件覆盖几个同步文件夹中的旧文件。软件带有一个小型数据库，监视每次更新后的文件状态。如果在一次同步之后，你删除了同步文件夹中某些文件，它在同步的时候将其它的几个文件夹的副本也删除，而不会将不需要的未删除文件重复拷贝到已更新的文件夹。由于软件自己会对文件进行删除和覆盖，它提供了使用回收站进行文件备份的措施，使用者可以在不慎执行错误的同步动作之后，从回收站将错误删除或覆盖的文件找回来（默认禁用该功能，请到软件选项处激活相应设置）。 主程序在 AllwaySync＼Bin＼里面，此为多国语言版，在语音选项那里选择中文即可。不过退出的时候会有错误提示（貌似没影响？）

备份软件使用方法v1.0

备份软件使用方法 一Bestsync2012使用说明 1 软件运行 点击BestSync2012运行软件 2 设置任务 在编辑菜单下点击追加任务（如果任务列表下没有任务可以在文件菜单下选择新建任务选项） 软件会弹出任务窗口，用来设置同步任务

以其中一个任务为例

选择好同步的文件夹和同步方向，点击下一步，按照要求设置任务即可。 3 查看任务 在以有任务中点击设置任务（任务必须是未在同步状态，否者不能点击设置任务选项）

点击后软件会弹出设置同步任务窗口，在这里可以在里面进行任务修改和设置

目前我们设置的同步任务只需要修改一般和日程两个窗口下的内容，其他暂时不需要修改。 BestSync2012这款同步软件目前还不是很稳定，需要不定期检查一下软件是否运行正常，如果发现软件出错，就关闭软件后在打开BestSync2012软件，因为打开软件后软件不会自动启动同步功能，所有需要手动启动所有任务 注意： 1 在修改任务在开启后，必须将修改的任务停止一下在开启，不然同步任务不能正常同步。 2 现有BestSync2012同步软件在16.15和151.247这两台机器上。

二Backup Exec 2010 R2 SP1使用说明 1 软件运行 点击Backup Exec 2010运行软件 2 设置任务 在作业设置选项中可以看到作业的作业名称、策略名称和备份选这项列表。 其中作业名称里放有现有作业，双击其中一个作业就可以看到作业属性。作业属性默认显示设备和介质窗口，在设备和介质窗口下可以选择设备和介质集。目前设备选项中因为只有一台磁带机工作，所有只有一个选项，而介质集一般选择永久保留数据-不允许覆盖选项。

管理软件使用说明书

目录 1 软件介绍...................................................... １ 2 软件运行环境 ................................................. １ 3 软件安装步骤 ................................................. １ 4 软件卸载步骤 ................................................. ４ 5 软件使用...................................................... ４5.1、创建数据库.............................................................................................................................. ４ 5.2、创建数据数据表................................................................................................................... ６ 5.3、历史数据读取 ........................................................................................................................ ７ 5.4、查看历史数据、通道信息.............................................................................................. ８ 5.5、打印数据、曲线或图片输出 .................................................................................... １３ 5.6、数据实时采集 .................................................................................................................... １５ 6 软件使用中可能出现的问题与解决方法.................. １８6.1、不出现对话框 .................................................................................................................... １８ 6.2、数据库不能建立............................................................................................................... １８ 6.3、U盘不能数据转存........................................................................................................... １８ 6.4、U盘上没有文件 ................................................................................................................ １８ 6.5、U盘数据不能导入计算机；...................................................................................... １８

GoodSync同步软件完美注册

GoodSync同步软件完美注册、本地同步图文教程 出处：西西整理作者：西西日期：2012-4-12 15:22:15 [大中小] 评论: 0 | 我要发表看法 文件管理这件看似简单的事，真的不简单，因为为了防止意外情况，你需要对文件进行备份，时间一久随着文件数量的增加，再加上有时也会临时队备份文件进行修改等。再想查出这个是最新的、文件有木有全部备份等….就没那么容易了吧！其实这一切说了很简单，因为你可以请：GoodSync软件来帮忙！ GoodSync是一款简单可靠的文件备份和文件同步软件，可以实现两台电脑或者电脑与U盘之间的数据文件的自动同步。GoodSync可以在本地U盘与电脑之间，以及U盘、移动硬盘或电脑与服务器、外部驱动器、W indowsM obile设备、网友、网盘等之间自动同步或单向备份数据。它能自动分析、同步、备份您的电子邮件、珍贵照片、联系人、电影视频、音乐文件、财务文件和其它重要文件。再也不会遗失您的电子邮件，照片，MP3等。 由于GoodSync为共享收费软件，所以这次西西带来的是官网原版+注册机（下载地址，下载的压缩包内含官网下载的GoodSync v9.1.5.5主程序和注册机以及注册说明），还是那句老话：如果你有能力请支持购买正版的GoodSync，如果….就低调吧！好吧！一起来看下注册方法吧！ GoodSync 注册方法： 1、首先下载压缩包，并解压运行GoodSync-Setup.exe 进行软件安装，软件默认安装为英文，如果要安装简体中文版，在安装时注意选择语言为：simpchinese项，安装完毕后运行GoodSync程序。 2、将你电脑的系统时间设置到2011年。 3、如下图所示，在软件主界面依次点击选择：帮助→ 激活专业版。

东莞二期投标文件管理软件操作手册V2.0.0.3

投标文件管理软件（V2.0.0.3） 用 户 使 用 手 册 深圳市斯维尓科技有限公司 二〇一三年三月五日

目录 1引言 (3) 2 程序运行环境 (4) 3 程序安装 (4) 4 软件启动 (9) 5软件整体说明 (12) 6 软件操作说明 (15) 6.1导入查看招标文件 (15) 6.2新建投标文件 (16) 6.3投标文件的管理功能 (23) 6.4校对工程量清单 (29) 6.5转换投标文件 (30) 6.6 电子签章 (32) 6.7生成投标文件 (34) 6.8查看数字签名信息 (41) 7 程序卸载 (42)

1引言 编写本手册的主要目的是为东莞市建设工程交易中心电子评标系统的投标文件管理软件的使用提供帮助。 投标文件管理软件主要提供给投标单位使用。投标单位通过投标文件管理软件将工程招标文件的一些主要内容导出，根据招标要求制作投标文件；加入已经制作好的工程投标文件所包含的所有文档（包括：技术标文件、工程量清单、工程图纸以及其它文件等），并进行管理，对文件包进行CA数字签名以防篡改，并生成压缩加密的电子投标文件包的功能。 投标文件管理软件的使用总体流程如下图所示：

2 程序运行环境 ?硬件环境：CPU: P4 2GHZ 内存2G,硬盘80GB ?软件环境：Windows 2000/XP/Windows Server 2003 ?软件支持：OFFICE2007+PDF转换插件/OFFICE2010 ?网络环境：带宽10/100Mbps 3 程序安装 东莞市建设工程交易中心网站(https://www.wendangku.net/doc/0f13179345.html,/)上下载最新安装包，点击安装程序，安装程序引导用户进行系统安装，主要有以下步骤： 一、启动安装程序，进入安装系统欢迎界面。如下图：

个人文件同步备份FILEGEE

软件简介： FileGee之软件主界面(图一) FileGee个人文件同步备份系统是一款优秀的文件同步与备份软件。它集文件备份、同步、加密、分割于一身。协助个人用户实现硬盘之间，硬盘与移动存储设备之间的备份与同步。强大的容错功能和详尽的日志、进度显示，更保证了备份、同步的可靠性。高效稳定、占用资源少的特点，充分满足了用户的需求。不需要额外的硬件资源，便能搭建起一个功能强大、高效稳定的全自动备份环境，是一种性价比极高的选择。 一.软件安装 FileGee个人文件同步备份系统在使用前须对其进行安装才可进行使用，软件须按照提示进行安装，软件安装过程如下图所示： FileGee之接受安装协议(图二) FileGee之选择安装目录(图三) FileGee之完成安装(图四) 二.软件使用 FileGee在完成安装后双击桌面图标即可启动该软件，用户如要创建备份，即可点击软件左上角处的新建任务按钮来创建新任务，软件提供多种任务类型，如单向同步，双向同步，镜像同步，更新同步等等，用户鼠标停留在任务类型上即可看到相关的解释说明，如下图所示： FileGee之新建任务(图五) 用户在选择创建备份任务类型后，即可点击下一步按钮，点击后软件会自动弹出窗口，用户需在窗口中设置要进行备份的文件夹所在位置，如下图所示： FileGee之设置备份文件夹(图六) 设置完要进行备份的文件夹后，我们还需要对备份文件的存储位置进行设置保存，另外为了节省空间我们还可以对文件设置是否进行压缩，如下图所示：

FileGee之备份文件保存(图七) 在设置完毕后我们即可点击下一步按钮，在后面的设置选项中我们还可以对备份的文件进行详细设置，如是否包含源目录的子目录，还可以根据文件名对要备份的文件进行过滤，也可以对文件进行过滤设置，如下图所示： FileGee之备份设置(图八) 设置完毕后，我们即可点击软件上侧列表中的开始按钮对文件进行备份，，另外还可以点击软件上侧的定时自动功能设置定时对文件夹进行自动备份，如下图所示： FileGee之备份任务(图九) 小结：FileGee作为一款免费得文件夹自动同步备份工具，不但功能上比较强大，在使用上也是非常的方便，如果您也需要一款文件备份工具的话，那么就来试试FileGee吧，只需简单几步就可以完成文件夹同步备份，非常方便!

说明书金助手美容美发管理软件操作手册连锁

说明书金助手美容美发管理软件操作手册连锁 Document number【AA80KGB-AA98YT-AAT8CB-2A6UT-A18GG】

金助手美容美发管理软件操作手册（连锁版） 后台设置 打开金助手管理系统进入主界面 操作员常用的功能都放在了主界面上，下面介绍下如何设置后台参数。打开主界面的右上角的按键，

一、基本参数设置 打开基本参数设置 在基本参数设置中进行本店卡种设置、基本卡种设置、基本折扣标准设置、基本工种设置和当前活动（现金）结帐执行的折扣标准。 首先在基本卡种设置中添加本店发行的所有会员卡种类（例如：本店发行金卡、银卡、钻卡和会员卡）同时系统会自动生成和卡种名称相同的折扣标准。 在基本工种设置中添加本店的员工的工种（例如：美容师、美发师、美甲师、助理等） 在当前活动（现金）结帐执行的折扣标准中添加散客所享受的折扣标准。（例如：店庆时，所有散客享受和金卡会员相同的折扣） 二、设置分店信息 设置分店信息是指多店连锁的情况下，在使用本软件的时候需要首先设置不同的分店信息。 （例如：***一店、***二店、***三店等） 点击对话框右侧的新增分店来添加分店信息 分店信息设置后了以后，同时设置分店对应的库房信息包括库房名称、编号、是否设置为默认销售出货库房等。

三、公共参数设置 打开公共参数设置，包括精确度方式、服务类别设置、商品类别设置、计次项目类别设置 部门设置、记事本数据类型、其他支付方式、其他积分类型以及缺勤原因等项目的设置。

精确度方式设置 这里的功能主要体现的是结帐时出现的零碎钱 （例如：顾客做了项目后结帐，原本顾客的价位是320元，折后的金额是元，折后的金额出现了元的零钱，这时我将系统设置成→，此时结帐的金额就会显示成为264元；当然我们也可以这样设置→，此时结帐的金额就会显示成为263元。同样的道理，264元中的4元零钱也可以用上面的方式进行取舍。）服务类别设置 设置服务类别时，最重要的是设置公共类别，设置了公共类别后可以更快捷，更方便的设置员工的提成系数。 分类一和分类二是可以按照项目的用途或是品牌来划分项目分类。 此功能主要是对服务项目的类别进行划分。操作员可以根据本店的自身情况进行设置。

备份与恢复应用

备份与恢复应用 备份技术 数据备份方式 从数据备份方式来说，主要有映像备份与逐文件备份两种方式。拓普恒基NAS产品主要采用的是逐文件备份方式。 通过进入文件系统，阅读文件结构，以及从一个介质到另一个介质复制文件，从而生成新文件结构。它可针对单独文件生成备份。逐文件备份比映像备份安全，因为整个文件结构都复制了。因而允许信息迁移入不同的格式或设备类型。逐文件备份还允许用户恢复个别文件或执行部分备份。在存在变化而信息无法恢复至同类介质的情况下，逐文件备份更安全。 逐文件备份通常恢复的时间要长于备份。当需要恢复单独文件和针对大型文件，如数据库文件时，建议使用逐文件备份。 数据备份策略 NAS在实现数据备份的时候能够支持两种备份策略，用户可以根据自己的应用环境来确定选用那种备份策略，在选择的时候，了解文档位的作用十分重要。文档位是一种标志，存在于每个文件中，以表明文件已完成修改的时间。一些备份设施使用文档位以跟踪文件备份状态和其他使用日志。 我们的技术支持的两种逐文件备份方式为：全备份和增量备份。 1、全备份： 全面系统备份将把所有文件、目录、用户信息、安全属性和系统/操作系统文件复制到备份设备。当执行全面系统备份时，无需检查文档位，因为所有文件都将备份。每个备份计划都应包括全面备份。 2、增量备份 增量备份只复制上次备份后发生变化的文件。备份软件将检查文档位，以确定文件是否被修改，以及是否需要备份。如果文件的文档位表明为新文件或已修改，文件将被复制到备份设备，文档位将清除。 两种逐文件备份方式的图示如下： 备份策略的选择并非完全以围绕数据备份的问题为基础，在选择最佳策略时也必须考虑到恢复的问题。

ERP管理软件操作手册

1.软件登录 双击进入看到如下界面，如图选择自己所需的公司帐套双击进入 操作 进入之后看到如下界面，如图选择自己的用户名（有密码输入密码）按键盘的F8或界面上的确认进入 进入界面看到如下界面，如图：左边为常用的报表查询单据；右边则为 常用单 据的操作及基础资料的设置；下方及一些软件自带按钮和右下角显示公司名称和登录用户名 2.单据的通用界面及功能键介绍 如下图：第一排：单据常用功能按钮；第二排：单据表头上方操作界面；第三排：单据表身操作界面；第四排：单据表头下方操作界面 下图为常用功能键的功能介绍；新增：添加新单据时使用；速查:查找原有的单据使用；编辑：修改原有单据使用；删除：删除单据使用;打印：打印出来使用；存盘：单据新增、修改后保存使用 3.单据操作指导 如下图，此界面为技术部业务的常用操作单据及报表总体介绍。 增加货品资料操作。 点击进入界面，如图操作。

查询原有货品资料操作。 查询结果 查询条件多种，根据实际方便来操作。 4.关于虚拟货品的替代件的输入操作。 点击进去 基本操作如查询跟货品操作一样，不一一讲解。 两种替代方式介绍 1.补量替换： 2.全量替换： 替代比例； 5.建立标准成品BOM的操作. 点击进入界面如下图。 增加BOM操作跟货品操作步骤一致，如图： 当BOM确定后，还未产生后续操作时，需修改BOM。如下操作。

当单据已经产生后续操作时，要修改该BOM则进入 进入操作。 三种活动方式介绍 1.增加：在原有BOM 里增加新的物料； 2.删除: 删除原有BOM里的物料； 3.改变：改变原有BOM里的物料货品的数量等； 6.客户订制品BOM的维护操作。 点击进入界面进行操作，如下图： 当单据已经产生后续操作时，要修改该订单配方则进入进入 操作，操作方法跟一样，就不一一细讲。

网络同步备份镜像备份软件使用分享

SyncBackPro网络同步备份软件教程 单位：华兴科软-技术服务部部门经理：余海教材开发：李江涛 不管你是不是一名网络技术运维工程师，你一定想过想把你家里的电脑与办公室的电脑文件能够保持同步，不要每天带着U盘把文件拖来拖去，不管你是不是想让你的个人电脑与办公室电脑的文件能够实时同步，你因为也许误删了一些文件，无法恢复想把自己的手剁下来喂狗。不管怎么样你总是会遇到这样那样的情况需要同步或者备份你当前电脑中的文件或者资料，但是百度网盘取消了文件夹同步功能，360网盘上传速度让人捉急。Linux上干脆连以上两款网盘备份软件都没有，可怜的你遇到问题只能默默地躲在厕所里哭泣。 今天本教程就是要拯救你，拯救受苦受难的大众，通过SyncBackPro你将能达成所愿，不再后悔，不再哭泣，让你不花钱也能实现普通人的容灾备份，长话短说，下面我们正式开始。 首先，你当现使用的必须是一台win7及以上版本的电脑，然后你在下面网址处 http://www.dayanzai.me/syncbackpro.html下载并且按照说明安装该软件，当然最好使用正版，这样才能获得长期稳定的更新和维护。 按照以上要求完成软件安装后，你在开始菜单和左面中都找不到软件的快捷方式——没关系！，点击开始菜单在所用程序/所有应用中找到2BrightSparks这个文件夹，点击进去，就能看到软件的快捷方式，当然你可以把它发送到桌面上，全凭你个人的意愿。 图一

图二 通过快捷方式打开软件会看到如下界面 好吧，我承认这个界面确实有点单调，不过一会儿你就不会这么觉得了，你可以在上方的菜单点击【同步任务】—【添加】来添加你第一项备份或者同步任务也可以点击下方的快捷菜单中的【添加】来操作。点击【添加】后如下图所示：你需要输入你的任务名称，这个根据你实际的需求来写，比如：文件备份。

备份与恢复管理相关的安全管理制度

信息系统备份与恢复管理 第一章总则 第一条为保障公司信息系统的安全，使得在计算机系统失效或数据丢失时，能依靠备份尽快地恢复系统和数据，保护关键应用和数据的安全，保证数据不丢失，特制定本办法。 第二条对于信息系统涉及到的网络设备、网络线路、加密设备、计算机设备、应用系统、数据库、维护人员，采取备份措施，确保在需要时有备用资源可供调配和恢复。 第三条本管理办法中涉及到的设备主要指运行在信息技术部主机房中的网络设备、加密设备及计算机设备。

第四条信息系统备份手段根据不同信息的重要程度及恢复时间要求分为实时热备份和冷备份等。同一平台的系统应尽量使用同样的备份手段，便于管理和使用。信息技术部负责信息系统的备份与恢复管理，并制定数据备份计划，对数据备份的时间、内容、级别、人员、保管期限、异地存取和销毁手续等进行明确规定。第五条信息技术部应根据各系统的重要程度、恢复要求及有关规定要求制定系统配置、操作系统、各应用系统及数据库和数据文件的备份周期和保存期限。 第六条对于重要系统和数据的备份周期及备份保存期限应遵循以下原则： (一) 至少要保留一份全系统备份。 (二) 每日运行中发生变更的文件，都应进行备份。

(三) 生产系统程序库要定期做备份，每月至少做一次。 (四) 生产系统有变更时，须对变更前后的程序库进行备份。 (五) 批加工若有对主文件的更新操作，则应进行批加工前备份。 (六) 每天批加工结束后都要对数据文件进行批后备份，对核心数据须进行第二备份。 (七) 对批加工生成的报表也要有相应的备份手段，并按规定的保留期限进行保留。 (八) 用于制作给用户数据盘的文件应有备份。 (九) 各重要业务系统的月末、半年末、年末以及计息日等特殊日的数据备份须永久保留。 (十) 定期将生产系统的数据进行删减压缩，并将删减的数据备份上磁带，永久保留。 (十一) 以上未明确保存期限的各项备份的保存至少应保存一周。

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单机版管理软件使用手册 NVR/Single Server 使用手册 尊敬的用户，专门感谢您一直对我公司的产品的关注，假如您在使用过程中按照使用手册无法解决问题时，请致电我公司技术部垂询相关操作方法。本手册的内容将做不定期的更新，恕不另行通知。

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自同步-局域网文件同步备份软件(Windows)使用说明-

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