Energy-Constrained Information Storage on Memristive Devices in the Presence of Resistive Drift
- URL: http://arxiv.org/abs/2501.10376v1
- Date: Mon, 16 Dec 2024 10:40:13 GMT
- Title: Energy-Constrained Information Storage on Memristive Devices in the Presence of Resistive Drift
- Abstract summary: We study the problem of information storage on memristors affected by resistive drift noise under energy constraints.<n>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.<n>We propose a joint source-channel coding (JSCC) approach to storing images in an analogue fashion.
- Score: 41.66366715982197
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- SoLAR: Error-Resilient Streamable Long-Horizon Free-Viewpoint Video Reconstruction with Anchor Activation and Latent Recalibration [57.159190580279585]
Motivated by bit allocation theory, we analyze dynamic-anchor-based volumetric video representation within a rate-distortion optimization framework.<n>We propose textbfSoLAR, which is the first error-resilient streamable FVV framework that maintains stable reconstruction quality on long sequences.
arXiv Detail & Related papers (2026-05-08T06:48:59Z) - Contextual Memory-Enhanced Source Coding for Low-SNR Communications [5.701461718918883]
This paper proposes a Memory-Augmented Source Coding scheme for robust transmission.<n>Rather than treating context as external side information, MASC internalizes contextual patterns into a source model.<n> MASC refines source probability estimation, shortens average codelength, and mitigates the sensitivity of source decoding to residual channel errors.
arXiv Detail & Related papers (2026-05-06T01:44:24Z) - Unlocking High-Fidelity Analog Joint Source-Channel Coding on Standard Digital Transceivers [54.82164616081277]
D2AJSCC is a novel framework enabling high-fidelity analog J SCC deployment on standard digital PHYs.<n>Our system achieves near-ideal analog J SCC performance with graceful degradation across SNR conditions.<n>By enabling next-generation semantic transmission on legacy infrastructure without hardware modification, our framework promotes sustainable network evolution.
arXiv Detail & Related papers (2026-03-10T01:42:23Z) - Joint Source-Channel-Generation Coding: From Distortion-oriented Reconstruction to Semantic-consistent Generation [58.67925548779465]
We propose Joint Source-Channel-Generation Coding (JSCGC), a novel paradigm that shifts the focus from perceptual reconstruction to probabilistic generation.<n>JSCGC improves substantially semantic quality and semantic fidelity, significantly outperforming conventional distortion-oriented J SCC methods.
arXiv Detail & Related papers (2026-01-19T08:12:47Z) - Context Video Semantic Transmission with Variable Length and Rate Coding over MIMO Channels [49.624608869195065]
We propose the context video semantic transmission (CVST) framework for wireless video transmission.<n>We learn a context-channel correlation map to explicitly formulate the relationships between feature groups and multiple input multiple output (MIMO) subchannels.<n>We demonstrate substantial performance gains over various standardized separated coding methods and recent wireless video semantic communication approaches.
arXiv Detail & Related papers (2025-12-23T10:48:43Z) - Learned Digital Codes for Over-the-Air Computation in Federated Edge Learning [42.73991180442414]
Federated edge learning (FEEL) enables wireless devices to collaboratively train a centralised model without sharing raw data.<n>OTA aggregation alleviates this by exploiting the superposition property of the wireless channel, enabling simultaneous transmission and merging communication with computation.<n>This work proposes a learned digital OTA framework that improves recovery accuracy, convergence behaviour, and robustness to challenging SNR conditions.
arXiv Detail & Related papers (2025-12-22T15:01:41Z) - VQ-DeepISC: Vector Quantized-Enabled Digital Semantic Communication with Channel Adaptive Image Transmission [8.858565507331395]
Discretization of semantic features enables interoperability between semantic and digital communication systems.<n>We propose a vector quantized (VQ)-enabled digital semantic communication system with channel adaptive image transmission.
arXiv Detail & Related papers (2025-08-01T02:35:34Z) - A Memory-Based Reinforcement Learning Approach to Integrated Sensing and Communication [52.40430937325323]
We consider a point-to-point integrated sensing and communication (ISAC) system, where a transmitter conveys a message to a receiver over a channel with memory.<n>We formulate the capacity-distortion tradeoff for the ISAC problem when sensing is performed in an online fashion.
arXiv Detail & Related papers (2024-12-02T03:30:50Z) - Towards Stabilized and Efficient Diffusion Transformers through Long-Skip-Connections with Spectral Constraints [51.83081671798784]
Diffusion Transformers (DiT) have emerged as a powerful architecture for image and video generation, offering superior quality and scalability.<n>DiT's practical application suffers from inherent dynamic feature instability, leading to error amplification during cached inference.<n>We propose Skip-DiT, an image and video generative DiT variant enhanced with Long-Skip-Connections (LSCs) - the key efficiency component in U-Nets.
arXiv Detail & Related papers (2024-11-26T17:28:10Z) - Diffusion-Driven Semantic Communication for Generative Models with Bandwidth Constraints [66.63250537475973]
This paper introduces a diffusion-driven semantic communication framework with advanced VAE-based compression for bandwidth-constrained generative model.<n>Our experimental results demonstrate significant improvements in pixel-level metrics like peak signal to noise ratio (PSNR) and semantic metrics like learned perceptual image patch similarity (LPIPS)
arXiv Detail & Related papers (2024-07-26T02:34:25Z) - Non-Coherent Over-the-Air Decentralized Gradient Descent [0.0]
Implementing Decentralized Gradient Descent in wireless systems is challenging due to noise, fading, and limited bandwidth.
This paper introduces a scalable DGD algorithm that eliminates the need for scheduling, topology information, or CSI.
arXiv Detail & Related papers (2022-11-19T19:15:34Z) - Deep Reinforcement Learning for IRS Phase Shift Design in
Spatiotemporally Correlated Environments [93.30657979626858]
We propose a deep actor-critic algorithm that accounts for channel correlations and destination motion.
We show that, when channels aretemporally correlated, the inclusion of the SNR in the state representation with function approximation in ways that inhibit convergence.
arXiv Detail & Related papers (2022-11-02T22:07:36Z) - DeepJSCC-Q: Constellation Constrained Deep Joint Source-Channel Coding [6.55705721360334]
We show that DeepJSCC-Q can achieve similar performance to prior works that allow any complex valued channel input.
DeepJSCC-Q preserves the graceful degradation of image quality in unpredictable channel conditions.
arXiv Detail & Related papers (2022-06-16T11:43:50Z) - STIP: A SpatioTemporal Information-Preserving and Perception-Augmented
Model for High-Resolution Video Prediction [78.129039340528]
We propose a Stemporal Information-Preserving and Perception-Augmented Model (STIP) to solve the above two problems.
The proposed model aims to preserve thetemporal information for videos during the feature extraction and the state transitions.
Experimental results show that the proposed STIP can predict videos with more satisfactory visual quality compared with a variety of state-of-the-art methods.
arXiv Detail & Related papers (2022-06-09T09:49:04Z) - Error Correction Code Transformer [92.10654749898927]
We propose to extend for the first time the Transformer architecture to the soft decoding of linear codes at arbitrary block lengths.
We encode each channel's output dimension to high dimension for better representation of the bits information to be processed separately.
The proposed approach demonstrates the extreme power and flexibility of Transformers and outperforms existing state-of-the-art neural decoders by large margins at a fraction of their time complexity.
arXiv Detail & Related papers (2022-03-27T15:25:58Z) - Nonlinear Transform Source-Channel Coding for Semantic Communications [7.81628437543759]
We propose a new class of high-efficient deep joint source-channel coding methods that can closely adapt to the source distribution under the nonlinear transform.
Our model incorporates the nonlinear transform as a strong prior to effectively extract the source semantic features.
Notably, the proposed NTSCC method can potentially support future semantic communications due to its vigorous content-aware ability.
arXiv Detail & Related papers (2021-12-21T03:30:46Z) - Energy-Efficient Model Compression and Splitting for Collaborative
Inference Over Time-Varying Channels [52.60092598312894]
We propose a technique to reduce the total energy bill at the edge device by utilizing model compression and time-varying model split between the edge and remote nodes.
Our proposed solution results in minimal energy consumption and $CO$ emission compared to the considered baselines.
arXiv Detail & Related papers (2021-06-02T07:36:27Z) - Capacity-Approaching Autoencoders for Communications [4.86067125387358]
The current approach to train an autoencoder relies on the use of the cross-entropy loss function.
We propose a methodology that computes an estimate of the channel capacity and constructs an optimal coded signal approaching it.
arXiv Detail & Related papers (2020-09-11T08:19:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.