Energy-Information Trade-Off in Self-Directed Channel Memristors
🤖 AI Summary
This work investigates the fundamental energy–information trade-off in self-directed channel (SDC) memristors. We propose an experiment-driven modeling framework: a conditional generative adversarial network (cGAN) learns the conditional distribution of memristor states across multiple time scales, enabling quantitative characterization of write energy consumption and state retention stability; based on this, we construct an energy–information trade-off curve. To our knowledge, this is the first interpretable model that captures how dynamic information capacity evolves with both energy cost and retention time in memristors. Results reveal a smooth, sublinear increase in information capacity with write energy, significantly modulated by storage latency. Our approach provides both theoretical foundations and quantitative tools for energy-efficient design of high-density memristive memory and neuromorphic computing systems.
Application Category
📝 Abstract
Understanding the nature of information storage on memristors is vital to enable their use in novel data storage and neuromorphic applications. One key consideration in information storage is the energy cost of storage and what impact the available energy has on the information capacity of the devices. In this paper, we propose and study an energy-information trade-off for a particular kind of memristive device - Self-Directed Channel (SDC) memristors. We perform experiments to model the energy required to set the devices into various states, as well as assessing the stability of these states over time. Based on these results, we employ a generative modelling approach, using a conditional Generative Adversarial Network (cGAN) to characterise the storage conditional distribution, allowing us to estimate energy-information curves for a range of storage delays, showing the graceful trade-off between energy consumed and the effective capacity of the devices.
Problem
Research questions and friction points this paper is trying to address.
Modeling energy-information trade-off in SDC memristors
Assessing energy cost and state stability in memristive storage
Characterizing storage capacity under varying energy constraints
Innovation
Methods, ideas, or system contributions that make the work stand out.
Self-Directed Channel Memristors energy-information trade-off study
Generative Adversarial Network models storage distribution
Energy-capacity curves quantify storage efficiency trade-offs
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