Domain decomposition is the process of identifying patterns of functionally repetitive, but independent, computation on data. This is the most common type of decomposition in the case of throughput computing, an
Example of step decomposition. An N point signal is broken into N signals, each consisting of a step function Even/Odd Decomposition The even/odd decomposition, shown in Fig. 5-14, breaks a signal into two component signals, one having even symmetry and the other having odd symmetry. An N...
Complex-valued neural networks have many advantages over their real-valued counterparts. Conventional digital electronic computing platforms are incapable of executing truly complex-valued representations and operations. In contrast, optical computing pl
A higher degree of accuracy (at the price of a further reduced transferability) is observed for potentials individually parameterized for specific molecular fragments. This, for example, applies to part of the MOF-FF family of potentials, which are parameterized for specific organic and inorganic MOF...
including China Mobile, which has made computing networks part of its company strategy. To make mobile computing networks reality, we need to seek innovation in network architectures and make further advances in computing technologies, including computing power measurements and computing task decomposition...
Problems are known that are contained in both classes and are believed to be outside of\(\mathsf {BPP}\). One such example isfactoring. Shor’s polynomial-time quantum algorithm for factoring demonstrates that the problem is in\(\mathsf {BQP}\)[14]. Additionally, for any number to be ...
2-by-2 unitary submatrices. For example, a 4-by-4 unitary matrix can be written as , where Here, is a 2-by-2 unitary matrix controlled by one MZI with three phase shifts, as discussed earlier. This matrix decomposition can be intuitively understood by regarding a general rotation in the...
For this reason, in this paper, we present an alternative heuristic decomposition approach that decomposes the bilevel lower-level problem into smaller sub-problems (each sub-problem is composed of a follower) which are solved to optimality. Then, these solutions are used for computing an ...
Clearly, the decomposition algorithm yields a scheme [x1y1,…, xkyk] and corresponding G-decomposition B^i+⋯+B^k for any undirected graph G. Moreover, if yixi had been chosen instead of xiyi some i, then Bi−1 would replace Bi in the G-decomposition. Applying the algorithm to the...
Fig. 2.6. Data arrangement for convolution acceleration in the systolic array. The convolutional operation in Fig. 2.2 is used as an example to explain the process of computing in a systolic array. In this example, we use fixed convolution kernel weights, and the input feature values and parti...