Memristor Overwrite Logic (MOL) for Energy-Efficient In-Memory DNN

In-memory computing is a promising solution to address the memory wall challenges in future processing systems. Substantial improvement in performance and energy efficiency is expected, in particular for data intensive applications. A typical use case is neural network applications, where large amount of data should be processed and moved between memory and processing cores. Although several recent works tried to accelerate processing through dedicated parallel hardware designs, data movement cost is still a critical technical challenge. In this context, we propose a novel programmable architecture design for in-memory deep neural networks (DNN) computation. Based on a new logic design style, namely Memristor Overwrite Logic (MOL), specialized computational memory is designed. The original architecture of the proposed computational memory allows to execute multiply-accumulate operations between stored words using MOL. Outstanding features are demonstrated with respect to other recent logic design styles based on emerging non-volatile memory technologies.