High-Accurate Stochastic Computing for Artificial Neural Network by Using Extended Stochastic Logic
Kun-Chih Jimmy Chen, Chi-Hsun Wu · 2021
The Artificial Neural Network (ANN) already shows the superiority in many real-world applications. However, due to the high dense neuron computing, the power issue becomes the design challenge of the ANN hardware implementation. On the other hand, the Stochastic Computing (SC) method has been proven as an efficient way to substitute the high-power arithmetic unit through stochastic bit-stream- based computing. Therefore, many SC-based ANN designs were proposed in recent years. However, due to the stochastic bitstream computing, the conventional SC-based ANN designs suffer from low computing accuracy. In this work, we apply the Extended Stochastic Logic (ESL) method to solve the accuracy problem of the conventional SC-based ANN designs. Because the ESL method supports a wider input coding range for the SC process, the computing accuracy can be improved. With this design concept, we propose an ESL-based adder to substitute the accumulation process in ANN computing. Furthermore, an ESL-based ReLU function is proposed to be used as the involved activation function instead. Compared with the conventional SC-based approaches, the proposed ESL-based ANN approach can help to improve the system accuracy by 48%. In addition, compared with the non-SC-based ANN, the proposed ESL- based ANN can reduce 84% area cost and 60% power consumption.