Stochastic Computing Design and Implementation of a Sound Source Localization System.

Stochastic computing (SC) is an alternative computing paradigm that processes data in the form of uniform bit-streams. SC is fault-tolerant and can compute on small, efficient circuits. However, SC is primarily used in scientific research, and its practical implementations for end-users are rare. Digital sound source localization (SSL) is a useful signal processing technique that locates speakers using multiple microphones. SC has not been integrated into SSL in practice or theory. In this work, for the first time to the best of our knowledge, we implement an SSL algorithm in the stochastic domain and develop a functional SC-based sound source localizer. The practical part of this work shows that the proposed stochastic circuit does not depend on conventional analog-to-digital conversion and can process data in the form of pulse-width-modulated (PWM) signals. The proposed SC design consumes up to 39% less area than the conventional binary design. It can also consume less power depending on the computational accuracy, for example, 6% less power consumption for 3-bit inputs. We propose a new cross-correlation (CC) design based on the state-of-the-art Sobol bit-streams for further area and power saving. The proposed design utilizes a MUX unit for bit-stream generation. It saves the area footprint up to 64% and the power consumption up to 82% compared to the counter-based SC design of CC, which relies on a comparator for bit-stream generation. The presented stochastic circuits, are not limited to SSL and are readily applicable to other practical applications such as radar ranging, wireless location, sonar direction finding, beamforming, and sensor calibration. The project’s source code is made available for public access.