High Reliable and Accurate Stochastic Computing-based Artificial Neural Network Architecture Design

Hardware reliability has emerged as a paramount consideration in the modern Artificial Neural Network (ANN) design in recent years. The un-reliable ANN leads to un-trustable inference results and disasters (e.g., finance system or transportation system crash). To ensure hardware reliability, the common way is to insert some error correctness blocks or fault-tolerant computing blocks, which bring considerable hardware overhead and are improper to the resource-limited edge AI designs. To design hardware-friendly and highly reliable hardware, the Stochastic Computing (SC) method has been proven to be an efficient way to achieve fault-tolerant computing goals. Consequently, many SC-based computing architectures have been introduced recently. However, because of the stochastic number representation, the computing accuracy issue is the design challenge to implement the SC-based computing architecture. To solve this problem, we propose a novel scaling-free adder and input data pre-processing method to achieve a reliable SC-based computing architecture and improve the accuracy of conventional SC-based ANN design. Compared with the traditional ANN design, the proposed SC-based ANN design maintains computing accuracy and enhances the performance by 32% to 55% while facing serious fault injection. In addition, the proposed SC-based ANN architecture reduces 48% to 81% power consumption and 51% to 92% area cost compared with the conventional ANN architecture.