مشخصات پژوهش

عنوان	Low Cost Implementation of Neural Networks Based on Stochastic Computing
نوع پژوهش	مقاله ارائه شده کنفرانسی
کلیدواژه‌ها	multi-layer perceptron, random number generator, stochastic computation, probability estimator
چکیده	Implementation of a multi-layer perceptron (MLP) by conventional circuits requires a very complex a hardware demanding circuit design. Recently, stochastic computation elements (SC) show the great potential to implement MLP with a low hardware complexity. The conversion between binary numbers and stochastic sequences has dramatic effect on accuracy and speed of an MLP implementation. In this paper, two novel architectures for digital to probability converter (DPC) and probability to digital converter (PDC) are proposed to improve the accuracy and the precision of an MLP implantation. The novel DPC performs the conversion by higher precision than the previous ones. The stochastic sequences converts to their corresponding binary numbers by 100% precision using the proposed PDC. Furthermore, the required number of clocks to generate the binary numbers are lower than the best method (~𝟎. 𝟏 𝐛𝐲 𝐚𝐯𝐞𝐫𝐚𝐠𝐞) and are equal for all of the numbers.
پژوهشگران	احمد منبری (نفر اول)، هادی جهانی راد (نفر دوم)