Dates
General information
Recent studies in machine learning have shown that large neural networks can dramatically improve the network performance, however, large networks are problematic in terms of computation time and memory usage for von Neumann computing architectures. On the other hand, modern computers are 10^4-10^8 times more power-hungry and less effective than the human brain for a wide range of tasks including perception, communication, and decision making. Therefore, developing computers that combine, learn, and analyze vast amounts of information quickly and efficiently is becoming increasingly important. The main goal of LC-DNA is to realize a scalable, power-efficient, and computationally fast analog neural network using a nonlinear liquid crystal (LC) as an analog hidden layer in our optical architecture. To demonstrate the scalability, speed, and power efficiency of our approach, we will compare it with state-of-the-art CPU and GPU technologies on challenging benchmark tasks. We believe that our results within this project will pave a way for computing technologies toward numerous large-scale complex problems that are not achievable yet.
