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.
翻译:受限制的波尔兹曼机器(RBM)是一个能够解决诸如NP-Hard组合优化问题和整数因子化等各种困难任务的随机神经网络。 成果管理制结构也非常紧凑, 需要很少的权重和偏差。 这与其简单、 可平行的取样算法一起寻找这些问题的地面状态, 使成果管理制可以加速硬件。 但是, 对这些问题的成果管理制培训可能是一个重大挑战, 因为培训算法往往无法满足大问题大小和高效绘图的要求。 在这里, 我们提出了一种将成果管理制结合起来的方法, 以避免需要全面训练大问题。 我们还提出了使成果管理制更加易于操作的方法, 使该算法能够有效地绘制成一个基于FPGA加速器的加速器。 使用这个加速器, 我们可以显示硬件加速了16位数的因子化速度, 快速改进速度为10000x, 功率改进为32x 。