Evaluation of Ferroelectric Tunnel Junction memristor for in-memory computation in real world use cases
Machine learning algorithms are experiencing unprecedented attention, but their inherent computational complexity leads to high energy consumption. However, a paradigm shift in computing methods has the potential to address the issue. This shift could be a move towards analog in-memory computing, a method which uses Ohm’s and Kirchhoff’s Laws, and carries out the processing directly where data res
