论文标题
逻辑上合成的，硬件加速的，受限制的Boltzmann机器，用于组合优化和整数分解
Logically Synthesized, Hardware-Accelerated, Restricted Boltzmann Machines for Combinatorial Optimization and Integer Factorization
论文作者
论文摘要
受限的玻尔兹曼机器（RBM）是一个随机神经网络，能够解决各种困难的任务，例如NP-HARD组合优化问题和整数分解。 RBM体系结构也非常紧凑。需要很少的权重和偏见。这以及其简单，可行的采样算法用于查找此类问题的基础状态，使RBM适合硬件加速度。但是，RBM在这些问题上的培训可能会带来重大挑战，因为训练算法往往会因大型问题大小而失败，并且很难找到有效的映射。在这里，我们提出了一种将RBM相结合在一起的方法，以避免需要以其完整形式训练大型问题。我们还提出了使RBM更加可安装的方法，从而使算法有效地映射到基于FPGA的加速器。使用此加速器，我们能够以高精度显示16位数字的硬件加速分解，速度提高10000X，功率提高32X。
The Restricted Boltzmann Machine (RBM) is a stochastic neural network capable of solving a variety of difficult tasks such as NP-Hard combinatorial optimization problems and integer factorization. The RBM architecture is also very compact; requiring very few weights and biases. This, along with its simple, parallelizable sampling algorithm for finding the ground state of such problems, makes the RBM amenable to hardware acceleration. However, training of the RBM on these problems can pose a significant challenge, as the training algorithm tends to fail for large problem sizes and efficient mappings can be hard to find. Here, we propose a method of combining RBMs together that avoids the need to train large problems in their full form. We also propose methods for making the RBM more hardware amenable, allowing the algorithm to be efficiently mapped to an FPGA-based accelerator. Using this accelerator, we are able to show hardware accelerated factorization of 16 bit numbers with high accuracy with a speed improvement of 10000x and a power improvement of 32x.
