Embedded emerging memory technologies for neuromorphic computing: temperature instability and reliability

For the first time, the impact of temperature instability of resistive memory switching on potential neuromorphic computing applications has been extensively studied using eNVM-R and eNVM-M technologies developed on Intel 22FFL process. The reliability risk assessment shows that the effects of ambient temperature (e.g. resistance or conductance shifting with varying temperature) can lead to potential degradation of the neural network accuracy. Our results provide additional insight into device-level physical models and circuit-level design guidance for potential AI hardware applications.