- The crossbar structure of Resistive-switching random access memory (RRAM) arrays enabled the In-Memory Computing circuits paradigm, since they imply the native acceleration of a crucial operations in this scenario, namely the Matrix-Vector-Multiplication (MVM). However, RRAM arrays are affected by several issues materializing in conductance variations that might cause severe performance degradation. A critical
one is related to the drift of the low conductance states appearing
immediately at the end of program and verify algorithms that
are mandatory for an accurate multi-level conductance operation. In this work, we analyze the benefits of a new programming
algorithm that embodies Set and Reset switching operations to
achieve better conductance control and lower variability. Data
retention analysis performed with different temperatures for 168
hours evidence its superior performance with respect to standard programming approach. Finally, we explored the benefits
of using our methodology at a higher abstraction level, throughThe crossbar structure of Resistive-switching random access memory (RRAM) arrays enabled the In-Memory Computing circuits paradigm, since they imply the native acceleration of a crucial operations in this scenario, namely the Matrix-Vector-Multiplication (MVM). However, RRAM arrays are affected by several issues materializing in conductance variations that might cause severe performance degradation. A critical
one is related to the drift of the low conductance states appearing
immediately at the end of program and verify algorithms that
are mandatory for an accurate multi-level conductance operation. In this work, we analyze the benefits of a new programming
algorithm that embodies Set and Reset switching operations to
achieve better conductance control and lower variability. Data
retention analysis performed with different temperatures for 168
hours evidence its superior performance with respect to standard programming approach. Finally, we explored the benefits
of using our methodology at a higher abstraction level, through
the simulation of an Artificial Neural Network for image recognition task (MNIST dataset). The accuracy achieved shows higher
performance stability over temperature and time.…