Deterministic Methods for Stochastic Computing using Low-Discrepancy Sequences

Recently, deterministic approaches to stochastic computing (SC) have been proposed. These compute with the same constructs as stochastic computing but operate on deterministic bit streams. These approaches reduce the area, greatly reduce the latency (by an exponential factor), and produce completely accurate results. However, these methods do not scale well. Also, they lack the property of progressive precision enjoyed by SC. As a result, these deterministic approaches are not competitive for applications where some degree of inaccuracy can be tolerated. In this work we introduce two fast-converging, scalable deterministic approaches to SC based on low-discrepancy sequences. The results are completely accurate when running the operations for the required number of cycles. However, the computation can be truncated early if some inaccuracy is acceptable. Experimental results show that the proposed approaches significantly improve both the processing time and area-delay product compared to prior approaches.