Raytheon, Space and Airborne Systems Manhattan Beach, CA, USA.
Georgia Institute of Technology Atlanta, GA, USA.
Front Neurosci. 2014 May 7;8:86. doi: 10.3389/fnins.2014.00086. eCollection 2014.
A FIELD PROGRAMMABLE ANALOG ARRAY (FPAA) IS PRESENTED AS AN ENERGY AND COMPUTATIONAL EFFICIENCY ENGINE: a mixed mode processor for which functions can be compiled at significantly less energy costs using probabilistic computing circuits. More specifically, it will be shown that the core computation of any dynamical system can be computed on the FPAA at significantly less energy per operation than a digital implementation. A stochastic system that is dynamically controllable via voltage controlled amplifier and comparator thresholds is implemented, which computes Bernoulli random variables. From Bernoulli variables it is shown exponentially distributed random variables, and random variables of an arbitrary distribution can be computed. The Gillespie algorithm is simulated to show the utility of this system by calculating the trajectory of a biological system computed stochastically with this probabilistic hardware where over a 127X performance improvement over current software approaches is shown. The relevance of this approach is extended to any dynamical system. The initial circuits and ideas for this work were generated at the 2008 Telluride Neuromorphic Workshop.
提出了一种现场可编程模拟阵列 (FPAA)，作为一种能量和计算效率引擎：一种混合模式处理器，可使用概率计算电路以显著更低的能量成本编译功能。更具体地说，将表明任何动力系统的核心计算都可以在 FPAA 上以显著低于数字实现的每操作能量来计算。实现了通过电压控制放大器和比较器阈值可动态控制的随机系统，该系统计算伯努利随机变量。从伯努利变量中可以显示出指数分布的随机变量，并且可以计算任意分布的随机变量。通过模拟 Gillespie 算法来展示该系统的实用性，通过计算使用该概率硬件随机计算的生物系统的轨迹，显示出比当前软件方法提高了 127 倍的性能。这种方法的相关性扩展到任何动力系统。这项工作的初始电路和想法是在 2008 年特柳赖德神经形态学研讨会上产生的。
