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Many different encoding algorithms for systematic polar codes (SPC) have been introduced since SPC was proposed in 2011. However, the number of the computing units of exclusive OR (XOR) has not been optimized yet. According to an iterative property of the generator matrix and particular lower triangular structure of the matrix, we propose an optimized encoding algorithm (OEA) of SPC that can reduce the number of XOR computing units compared with existing non-recursive algorithms. We also prove that this property of the generator matrix could extend to different code lengths and rates of the polar codes. Through the matrix segmentation and transformation, we obtain a submatrix with all zero elements to save computation resources. The proportion of zero elements in the matrix can reach up to 58.5% from the OEA for SPC when the code length and code rate are 2048 and 0.5, respectively. Furthermore, the proposed OEA is beneficial to hardware implementation compared with the existing recursive algorithms in which signals are transmitted bidirectionally.

In this paper we propose a method for scaling up filterbased feature selection in classification problems. We use the conditional mutual information as filter measure and show how the required statistics can be computed in parallel avoiding unnecessary calculations. The distribution of the calculations between the available computing units is determined based on balanced incomplete block designs, a strategy first developed within the area of statistical design of experiments. We show the scalability of our method through a series of experiments on synthetic and real-world datasets.

Spin-glasses have become one of the most computing-demanding problems of the last 50 years in Statistical Physics. These extremely slow systems represent a clear example of an easy-to-describe but hard-to-simulate numerical problem. We have developed an FPGAs architecture, called Janus, able to exploit the simplicity of the problem by an extensive parallelization of the computing units. In this work we describe the architecture after motivating the problem. We give the performance figures compared with other more usual architectures. We have obtained a clear advantage in terms of computing power which produced several top results in the field. In addition, we describe the current development of the next generation of the infrastructure: Janus II.