The development of large-scale neuromorphic hardware has made practical implementations of threshold gate-based circuits a near-term possibility. The complexity advantages regarding traditional computing classes, as evidenced in the literature, have prompted us to tackle Epistasis Detection, one of the most computationally complex combinatorial problems in bioinformatics. We propose specially designed circuits that calculate the relative frequencies of all dataset combinations in an efficient pipelined fashion, taking advantage of co-located memory and configurable parallelism, obtaining complexity gains. Overall, we obtain the runtime to be bounded by the number of combinations to calculate, without any additional complexity overhead, contrary to classical approaches, using log-linear space. To accomplish this, we propose a data encoding and combination generation strategy using Leaky Integrate and Fire (LIF) neurons, that feeds a constant depth threshold gate population count circuit. Accounting for typical hardware characteristics, such as limited fan-in and variable precisions, we obtain logarithmic depth and log-cubic linear connections, for the population count circuit by composing developed unbounded fan-in constant depth threshold gate circuits to perform population count and binary array sum.

翻译：大规模神经形态硬件的发展使得基于阈值门的电路实用化成为近期可能。文献中展示的相对于传统计算类别的复杂度优势，促使我们着手解决上位性检测——生物信息学中计算复杂度最高的组合问题之一。我们提出了一种专门设计的电路，利用共置内存和可配置并行性，以高效流水线方式计算所有数据集组合的相对频率，从而获得复杂度收益。总体而言，与经典方法不同，我们实现了运行时间受限于待计算组合数量，且无需额外复杂度开销，同时使用对数线性空间。为实现这一目标，我们提出了一种采用漏积分点火（LIF）神经元的数据编码与组合生成策略，为恒定深度阈值门种群计数电路提供输入。考虑到有限扇入和可变精度等典型硬件特性，我们通过组合无界扇入恒定深度阈值门电路来执行种群计数和二进制数组求和，最终得到对数深度和对数立方线性连接数的种群计数电路。