Memristor energy storage calculation

How can memristors be used to create energy-efficient reservoir computing networks?

Tunable and stable memristors based on single-crystalline entropy-stabilized oxide films grown on epitaxial bottom electrodes can be used to create energy-efficient reservoir computing networks.

Does memristor modulation reduce energy consumption?

Estimation methodology for energy consumed by memristor is established. Energy comparisons for different learning strategies in various networks are touched. Less-pulses and low-power-first modulation methods are energy efficient. Proper decreasing the memristor modulation precision reduces the energy consumption.

What determines the energy consumption of a memristor?

As shown in Fig. 1 (e), the energy consumption of memristor is codetermined by many factors at the element device level, including material, device size and modulation signal.

How does a memristor determine the energy consumption of a synaptic device?

Furthermore, if the conductance of memristor moves from initial state G 1 to desired state G 2 in one modulation, the shaded current areas under identical modulation pulses equivalently denote the modulation energy consumption on this memristor. Fig. 2. Energy estimation for synaptic device based on memristor.

How does a memristor keep its resistance value?

A memristor retains its resistance value even when the power supply is disconnected. The most recently attained resistance is automatically saved in the memristor’s internal state, allowing it to resume its previous resistance value when power is restored.

Why is energy consumption more complex than memristor?

When concerning the energy consumption at the system level, the situations become much more complex than the memristor part, because of the lack of definite mathematical model for simulation and difficulty to quantitatively estimate the energy of each component individually.