2026-05-22T05:27:53Zhttps://repisalud.isciii.es/rest/oai/request
oai:repisalud.isciii.es:20.500.12105/232912024-11-29T05:03:56Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16967
Repisalud author Frasser, Christiam F author Roca, Miquel author Rossello, Josep L 2024-09-18T06:43:51Z 2024-09-18T06:43:51Z 2021-12 Frasser CF, Roca M, Rossello JL. Optimal Stochastic Computing Randomization. Electronics. 2021 Dec;10(23):2985. 10.3390/electronics10232985 2079-9292 Electronics https://hdl.handle.net/20.500.13003/19730 2-s2.0-85120324437 https://hdl.handle.net/20.500.12105/23291 735682500001 Stochastic computing (SC) is a probabilistic-based processing methodology that has emerged as an energy-efficient solution for implementing image processing and deep learning in hardware. The core of these systems relies on the selection of appropriate Random Number Generators (RNGs) to guarantee an acceptable accuracy. In this work, we demonstrate that classical Linear Feedback Shift Registers (LFSR) can be efficiently used for correlation-sensitive circuits if an appropriate seed selection is followed. For this purpose, we implement some basic SC operations along with a real image processing application, an edge detection circuit. Compared with the literature, the results show that the use of a single LFSR architecture with an appropriate seeding has the best accuracy. Compared to the second best method (Sobol) for 8-bit precision, our work performs 7.3 times better for the quadratic function; a 1.5 improvement factor is observed for the scaled addition; a 1.1 improvement for the multiplication; and a 1.3 factor for edge detection. Finally, we supply the polynomials and seeds that must be employed for different use cases, allowing the SC circuit designer to have a solid base for generating reliable bit-streams. eng Stochastic computing LFSR Seeding Correlation Optimal Stochastic Computing Randomization research article