Massively parallel annealing processors may offer superior performance for a wide range of sampling and optimization problems. A key component dictating the size of these processors is the neuron update circuit, ideally implemented using special stochastic nanodevices. We leverage photon statistics using single photon avalanche diodes (SPADs) and temporal filtering to generate stochastic states. This method is a powerful alternative offering unique features not currently seen in annealing processors: the ability to continuously control the computational temperature and the seamless extension to the Potts model, a $n$-state generalization of the two-state Ising model. SPADs also offer a considerable practical advantage since they are readily manufacturable in current standard CMOS processes. As a first step towards realizing a CMOS SPAD-based annealer, we have designed Ising and Potts models driven by an array of discrete SPADs and show they accurately sample from their theoretical distributions.
翻译:大规模平行肛交处理器可能为一系列广泛的取样和优化问题提供优异的性能。描述这些处理器大小的一个关键组成部分是神经更新电路,最好是使用特殊的随机纳米装置进行。我们利用光子统计数据,利用单光子雪崩二极管(SPADs)和时间过滤法生成随机状态。这种方法是一种强大的替代方法,提供了目前在整流处理器中看不到的独特性能:持续控制计算温度的能力和波茨模型的无缝扩展,对两州Ising模型采用一元价一价的通用。SPADs也提供了相当大的实际优势,因为它们在当前的标准 CMOS 工艺中很容易制造。作为实现基于CMOS SPAD 的脉冲器的第一步,我们设计了由一系列离散式SPADs驱动的Ising和波茨模型,并展示了它们理论分布的准确样本。