### Table 1: The von Neumann-Halperin Algorithm

2006

Cited by 2

### Table 3.1: Parameters for a conventional Von Neumann computer.

### TABLE 3.1. Von Neumann conditions for stability with -methods.

### Table 1. Output unreliabilitiesof a one-bit NAND multiplexing circuit (von Neumann fault).

2003

### Table 4.1: Output unreliabilities of a 1-bit NAND multiplexing circuit (von Neumann fault).

### Table 3. Wilcoxon signed rank tests. The table compares re-structuring with other methods. von Neumann, L-Best-1 and L-Best-2 were used with FIPS.

2005

Cited by 2

### Table[Part[HSbasematrices[n],i]//MatrixForm, CUi,1,Length[HSbasematrices[n]]CV]] In[86]:= (* * * AYmorphismQ[tau,d]AYis the predicate stating that AS : Md(C) AX Md(C) is a morphism of the Von Neumann algebra Md(C) * **)

2007

### Table 3: Results of the veri cation of the von Neumann adder then the remaining sum of RA and RB has only n bits, otherwise the sum may have n + 1 bits. The resulting goal can now be solved by strat- egy B and additional tautology checking. It only contains 2n + 1 variables and a length of O(n), such that the asymptotical complexity is now O(n22n). The fourth column of table 3 shows the detailed results of this approach.

1996

"... In PAGE 8: ... A speci cation of the circuit, whose veri cation is outlined in detail in the following, is therefore the following: rdy ^ req ! h C N A~ +B WHEN rdyi The veri cation of this speci cation can be done by various strategies though it is certainly given at bitvector level. Table3 shows the run- times (in secondes) and storages requirements (in BDD nodes) on a Sun Sparc 10 for some strategies.The rst column of table 3 contains the re- sults of the strategy HW B, i.... ..."

Cited by 4

### Table 1: Von Neumann computer versus biological computer. the other hand, a successful ANN may not have any resemblance to the biological system. Our ability to model biological nervous system using ANNs can increase our understanding of biological functions. For example, experimental psychologists have used neural networks to model classical conditioning animal learning data for many years [1]. The state-of-the- art in computer hardware technology (e.g., VLSI and optical) has made such modeling and simulation feasible. The long course of evolution has resulted in the human brain to possess many desirable characteristics which are present neither in a Von Neumann computer nor in modern paral- lel computers. These characteristics include massive parallelism, distributed representation and computation, learning ability, generalization ability, adaptivity, inherent contextual in- formation processing, fault tolerance, and low energy consumption. It is hoped that ANNs, motivated from biological neural networks, would possess some of these desirable character- 9

1996

"... In PAGE 8: ...roblems (e.g., recognizing a person in a crowd from a mere glimpse of his face) at such a fast speed and extent as to dwarf the world apos;s fastest computer. Why does there exist such a remarkable di erence in their performance? The biological computer employs a completely di erent architecture than the Von Neumann architecture (see Table1 ). It is this di erence that signi cantly a ects the type of functions each computational model is best able to perform.... ..."

Cited by 40

### Table 5 summarizes the results and gives the execution times of the PCGNR

1992

"... In PAGE 30: ... Table5 : Total number of iterations and execution time per iteration (titer) and the total execution time (ttotal), as a function of the relative refractive index nrel; m=0 means no preconditioning, m=1 is a first order von Neumann preconditioner. 6] SUMMARY AND DISCUSSION Our aim is to simulate the scattering of (visible) light by biological cells (specifically human white bloodcells).... ..."

Cited by 11