对概率协处理器进行基准测试
Benchmarking a Probabilistic Coprocessor
摘要：
在过去的几十年里，计算一直是由基于经典确定性比特的确定性计算机驱动的。最近，替代计算范式和基于域的计算（如量子计算和概率计算）已经获得了牵引力。虽然基于q比特的量子计算机利用量子效应来推进计算，但基于概率（p）比特的概率计算机自然适合于解决需要在蒙特卡洛和马尔可夫链蒙特卡洛算法中利用大量随机数的问题。这些蒙特卡罗技术用于解决优化，数值积分或概率分布抽样领域的重要问题。然而，为了有效地实现蒙特卡罗算法，随机数的生成是至关重要的。在本文中，我们提出了一个概率协处理器的基准测试的基础上，自然适合解决这些问题的p位。我们提出了多个例子和项目，我们的概率协处理器的纳米磁性实现可以优于经典的CPU和GPU实现的多个数量级。
英文摘要：
Computation in the past decades has been driven by deterministic computers based on classical deterministic bits. Recently, alternative computing paradigms and domain-based computing like quantum computing and probabilistic computing have gained traction. While quantum computers based on q-bits utilize quantum effects to advance computation, probabilistic computers based on probabilistic (p-)bits are naturally suited to solve problems that require large amount of random numbers utilized in Monte Carlo and Markov Chain Monte Carlo algorithms. These Monte Carlo techniques are used to solve important problems in the fields of optimization, numerical integration or sampling from probability distributions. However, to efficiently implement Monte Carlo algorithms the generation of random numbers is crucial. In this paper, we present and benchmark a probabilistic coprocessor based on p-bits that are naturally suited to solve these problems. We present multiple examples and project that a nanomagnetic implementation of our probabilistic coprocessor can outperform classical CPU and GPU implementations by multiple orders of magnitude.
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