A high-capacity and nonvolatile spintronic associative memory hardware accelerator

Significant progress has been made in manufacturing emerging technologies in recent years. This progress implemented in-memory-computing and neural networks, one of today's hottest research topics. Over time, the need to process complex tasks has increased. This need causes the emergence of intelligent processors. A nonvolatile associative memory based on spintronic synapses utilising magnetic tunnel junction (MTJ) and carbon nanotube field-effect transistors (CNTFET)-based neurons is proposed. The proposed design uses the MTJ device because of its fascinating features, such as reliable reconfiguration and nonvolatility. At the same time, CNTFET has overcome conventional complementary metal-oxide-semiconductor shortcomings like the short channel effect, drain-induced barrier lowering, and poor hole mobility. The proposed design is simulated in the presence of process variations. The proposed design aims to increase the number of weights generated in the synapse for higher memory capacity and accuracy. The effect of different tunnel magnetoresistance (TMR) values (100%, 200%, and 300%) on the performance and accuracy of the proposed design has also been investigated. This investigation shows that the proposed design performs well even with a low TMR value, which is very important and remarkable from the fabrication point of view.

Automated summary

Significant progress has been made in manufacturing emerging technologies, such as in-memory computing and neural networks. The proposed design of a nonvolatile associative memory based on spintronic synapses and carbon nanotube field-effect transistors shows promising features including reliable reconfiguration and nonvolatility. The design aims to increase memory capacity and accuracy by generating more weights in the synapse, and it performs well even with a low tunnel magnetoresistance (TMR) value, which is important from a fabrication perspective.