Bit-Parallel Implementations of Neural Network Activation Functions in Onboard Computing Systems

This study generalizes and further develops methods for efficiently implementing artificial neural networks (ANNs) in the onboard computers of mobile robotic systems with limited resources, including unmanned aerial vehicles (UAVs). The neural networks are sped up by constructing a new unbounded activation function called “s-parabola”, which meets the requirements of twice differentiability and reduces computational complexity over sigmoid-based functions. An additional contribution to this acceleration comes from activation functions based on bit-parallel computational circuits. A comprehensive review of modern publications in this subject area is provided. For autonomous problem-solving using ANNs directly on board an unmanned aerial vehicle, a trade-off between the speed and accuracy of the resulting solutions must be achieved. For this reason, we propose using fast bit-parallel circuits with limited digit capacity. The special representation and calculation of activation functions is performed based on the transformation of Jack Volder’s CORDIC iterative algorithms for trigonometric functions and Georgy Pukhov’s bit-analog calculations. Two statements are formulated, the proofs of which are based on the equivalence of the results obtained using the two approaches. We also provide theoretical and experimental estimates of the computational complexity of the algorithms achieved with different operand summation schemes.