Low-Power Continuous Wavelet Transform Employing Stochastic Computing

The continuous wavelet transform (CWT) is essential for analyzing non-stationary signals in edge computing, but traditional implementations are limited by high power demands, particularly in resource-constrained environments. Stochastic computing (SC), which leverages probabilistic bit-streams and compact arithmetic units, provides a promising alternative for ultra-low-power CWT designs. However, such circuits are vulnerable to transient faults and involve careful power-reliability trade-offs. This work presents the first SC-based CWT hardware design aimed at applications with severe resource constraints, including power consumption, accuracy, and fault tolerance. We comprehensively analyzed our design, which features a Sobol-based pseudo-random number source and accumulative parallel counter-based addition. In a fault-free environment, this design achieves an 84% power reduction over non-SC CWTs and a 37% reduction over other SC designs. It also achieves up to 64% area savings and reduces latency by 8×. Under a 30% fault rate, our design improves RMSE by 74% over binary CWTs and 32% over other SC implementations.