Compiling probabilistic, bio-inspired circuits on a field programmable analog array - Marr, Bo; Hasler, Jennifer (Frontiers in Neuroscience, 2014)
A field programmable analog array (FPAA) is presented as an energy efficiency engine: a mixed mode processor for which functions can be compiled at significantly less energy costs using noisy 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 dynamically controllable stochastic system is implemented, which computes Bernoulli random variables, exponentially distributed random variables, and allows for random variables of an arbitrary distribution to be computed. The utility of this system is demonstrated by implementing the well-known Gillespie algorithm for calculating the trajectory of an arbitrary biological system 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.