在能耗限制下，用神经网络优化存于忆阻器中的图像质量。
Energy-Constrained Information Storage on Memristive Devices in the Presence of Resistive Drift
- 用深度联合信源信道编码学习能量感知的编码策略。
- 延迟越长，图像重建质量下降，但新方法更稳定均衡。
- 适合关注低功耗存算一体与神经形态计算的研究者。
本文研究在能量约束和电阻漂移噪声影响下，忆阻器的信息存储问题。我们提出一种信息寿命与能耗之间的基本权衡关系，并将存储问题建模为受能量限制的噪声信道通信。为此，提出一种联合信源信道编码（JSCC）方案，以模拟方式存储图像。通过采用深度学习中的深度联合信源信道编码（DeepJSCC），结合生成式电阻漂移模型作为可微分的通道模型，实现端到端优化。我们引入一种改进的广义除法归一化（cGDN），使其能根据初始电阻状态和存储-读取延迟等连续信道特性进行条件调节。实验表明，该延迟条件网络能学习出更高效的能量感知编码方案，在不同存储延迟下均实现更高且更均衡的图像重建质量。
原文摘要 · Abstract (English)
In this paper, we examine the problem of information storage on memristors affected by resistive drift noise under energy constraints. We introduce a novel, fundamental trade-off between the information lifetime of memristive states and the energy that must be expended to bring the device into a particular state. We then treat the storage problem as one of communication over a noisy, energy-constrained channel, and propose a joint source-channel coding (JSCC) approach to storing images in an analogue fashion. To design an encoding scheme for natural images and to model the memristive channel, we make use of data-driven techniques from the field of deep learning for communications, namely deep joint source-channel coding (DeepJSCC), employing a generative model of resistive drift as a computationally tractable differentiable channel model for end-to-end optimisation. We introduce a modified version of generalised divisive normalisation (GDN), a biologically inspired form of normalisation, that we call conditional GDN (cGDN), allowing for conditioning on continuous channel characteristics, including the initial resistive state and the delay between storage and reading. Our results show that the delay-conditioned network is able to learn an energy-aware coding scheme that achieves a higher and more balanced reconstruction quality across a range of storage delays.
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