In this project, we propose an optoelectronic implementation of a Long Short-Term Memory (LSTM) layer. The optoelectronic technology is adopted in order to implement LSTM units in a stacked recurrent deep neural network. For the latter, we aim at introducing for the first time full analog computations that also cope with low-power constraints. The proposed system is based on standard microelectronic technology, while discrete-time delays are achieved via an optical line based on a long single mode optical fiber spool, lighted by a laser source tuned by a Mach-Zehnder modulator. This set-up allows a delay in the order of nanoseconds and adjustable by means of adapting the MZM parameters, thus there is no need for discrete quantization. Moreover, the low-power consumption, as well as the ability to perform computations with no finite precision and numerical constrains, are two prized features for embedded systems and smart sensors in the era of big data and, more in general, of real-world applications dealing with multiple data sources (i.e., IoT, Smart Grids, Intelligent Transport Systems, environmental control, home automation, e-health, and so forth). By the achievements obtained in the R&D activities and experimental assessments of this project, we will offer the opportunity to evaluate pros and cons of analog optoelectronic implementations, with respect to numerical approaches based on standard C-MOS technology, in terms of numerical performance, precision, power consumption, scalability, replicability, economical cost. Since, to our knowledge, there are no analog implementations of LSTM cells for Deep Learning proposed so far, either relying on optoelectronic technology, as in this project, or using microelectronic integrated components, the expected results may achieve an advancement of knowledge compared to the state of the art. Hence, the main goal of this project is to suggest a novel discrete-time analog implementation of recurrent deep neural networks.
