Neural networks in modern communication systems can be susceptible to
internal numerical errors that can drastically effect decision results. Such
structures are composed of many sections each of which generally contain
weighting operations and activation function evaluations. The safe use comes
from methods employing number based codes that can detect arithmetic errors in
the network's processing steps. Each set of operations generates parity values
dictated by a code in two ways. One set of parities is obtained from a
section's outputs while a second comparable set is developed directly from the
original inputs. The parity values protecting the activation functions involve
a Taylor series approximation to the activation functions. We focus on using
long numerically based convolutional codes because of the large size of data
sets. The codes are based on Discrete Fourier Transform kernels and there are
many design options available. Mathematical program simulations show our
error-detecting techniques are effective and efficient.

研究神经网络计算中的数值误差，并提出使用长数字卷积码时的激活函数误差检测技术。

神经网络安全使用

Safe Use of Neural Networks

In this paper, we propose a parallel block-based Viterbi decoder (PBVD) on
the graphic processing unit (GPU) platform for the decoding of convolutional
codes. The decoding procedure is simplified and parallelized, and the
characteristic of the trellis is exploited to reduce the metric computation.
Based on the compute unified device architecture (CUDA), two kernels with
different parallelism are designed to map two decoding phases. Moreover, the
optimal design of data structures for several kinds of intermediate information
are presented, to improve the efficiency of internal memory transactions.
Experimental results demonstrate that the proposed decoder achieves high
throughput of 598Mbps on NVIDIA GTX580 and 1802Mbps on GTX980 for the 64-state
convolutional code, which are 1.5 times speedup compared to the existing
fastest works on GPUs.

本文提出了一种并行块状 Viterbi 解码器，应用于 GPU 平台上，用于卷积码的译码。在 CUDA 架构的基础上，设计了两个不同并行性的内核来映射两个解码阶段，并针对几种中间信息的最优数据结构的设计，以提高内部内存交换的效率。实验结果表明，所提出的解码器在 NVIDIA GTX 580 上可实现 598Mbps 的高吞吐量，并且对于 64 状态卷积码，在 GTX980 上可以实现 1802Mbps 的解码速度，比现有 GPU 上的最快速度提高了 1.5 倍。

基于 GPU 的卷积码 Gb/s 级并行块 Viterbi 解码器

A Gb/s Parallel Block-based Viterbi Decoder for Convolutional Codes on  GPU

Humans have $23$ pairs of homologous chromosomes. The homologous pairs are
almost identical pairs of chromosomes. For the most part, differences in
homologous chromosome occur at certain documented positions called single
nucleotide polymorphisms (SNPs). A haplotype of an individual is the pair of
sequences of SNPs on the two homologous chromosomes. In this paper, we study
the problem of inferring haplotypes of individuals from mate-pair reads of
their genome. We give a simple formula for the coverage needed for haplotype
assembly, under a generative model. The analysis here leverages connections of
this problem with decoding convolutional codes.

本文研究从个体基因组的 mate-pair reads 中推断出其 haplotypes 的问题，并且给出了在生成模型下 haplotype assembly 所需的覆盖度的简单公式。该分析利用了该问题与解码卷积码的联系。