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Stochastic computing (SC) is a reemerging computing paradigm that oers low-cost and noise-resilient hardware designs for a variety of arithmetic functions. In SC, circuits operate on uniform bit-streams, where the value is encoded by the probability of observing ‘1’s in the stream. The accuracy of SC operations highly depends on the correlation between input bit-streams. Some operations, such as minimum and maximum, require highly correlated inputs, whereas others like multiplication demand uncorrelated or statistically independent inputs for accurate results. Developing low-cost and accurate correlation manipulation circuits is critical, as they allow correlation management without incurring the high cost of bit-stream regeneration. This work introduces novel in-stream correlator and decorrelator circuits capable of: 1) adjusting correlation between stochastic bit-streams and 2) controlling the distribution of ‘1’s in the output bit-streams. Compared to state-of-the-art (SoA) approaches, our designs oer improved accuracy and reduced hardware overhead. The output bit-streams enjoy low-discrepancy (LD) distribution, leading to higher quality of results. To further increase the accuracy when dealing with pseudo-random inputs, we propose an enhancement module that balances the number of ‘1’s across adjacent input segments. We show the eectiveness of the proposed techniques through two application case studies: SC design of sorting and median filtering.