A High-Accuracy Normalization Unit Using Multi-Bit Random Variables

Stochastic computing (SC) has the characteristics of low complexity and is expected to solve the bottleneck problem of conventional binary computing. Stochastic normalization units are widely used in stochastic decoding and stochastic signal detection, and have achieved hardware efficiency far exceeding conventional methods. However, they also have problems such as 1-bit representation and low calculation accuracy, calculation overflow, and fluctuation in the sum of normalized probabilities, which lead to prolonged processing latency and degraded hardware efficiency. Thus, this paper proposes a novel stochastic normalization unit using multi-bit random variables. Benefiting from the high representation accuracy of multi-bit random variables, the accuracy of the proposed unit is greatly improved. Meanwhile, the proposed unit completely solves the problems of calculation overflow and fluctuation in the sum of normalized probabilities. Simulation results show that the proposed 3-bit unit achieves a fourfold improvement in convergence speed and 2 times higher hardware efficiency compared to the state-of-the-art stochastic normalization unit. Finally, we verify that the proposed 3-bit unit demonstrates a 75% improvement in hardware efficiency for stochastic sparse-code multiple-access (SCMA) detection.