Deep reinforcement learning: Algorithm, applications, and ultra-low-power implementation

The DRL framework is mainly utilized for the complicated control problems and requires light-weight and low-power implementations in edge and portable systems. In order to achieve this goal, we develop the ultra-low-power implementation of the DRL framework using the stochastic computing technique, which has the potential of significantly enhancing the computation speed and reducing hardware footprint and therefore the power/energy consumption. The overall implementation is based on the effective stochastic computing-based implementations of approximate parallel counter-based inner product blocks and tanh activation functions. The stochastic computing-based implementation achieves only 57941.61 μm2 area and 6.30 mW power with 412.47 ns delay.