Channel Memory Research Articles

Highly Reliable 3D Channel Memory and Its Application in a Neuromorphic Sensory System for Hand Gesture Recognition.

Brain-inspired neuromorphic computing systems, based on a crossbar array of two-terminal multilevel resistive random-access memory (RRAM), have attracted attention as promising technologies for processing large amounts of unstructured data. However, the low reliability and inferior conductance tunability of RRAM, caused by uncontrollable metal filament formation in the uneven switching medium, result in lower accuracy compared to the software neural network (SW-NN). In this work, we present a highly reliable CoOx-based multilevel RRAM with an optimized crystal size and density in the switching medium, providing a three-dimensional (3D) grain boundary (GB) network. This design enhances the reliability of the RRAM by improving the cycle-to-cycle endurance and device-to-device stability of the I-V characteristics with minimal variation. Furthermore, the designed 3D GB-channel RRAM (3D GB-RRAM) exhibits excellent conductance tunability, demonstrating high symmetricity (624), low nonlinearity (βLTP/βLTD ∼ 0.20/0.39), and a large dynamic range (Gmax/Gmin ∼ 31.1). The cyclic stability of long-term potentiation and depression also exceeds 100 cycles (105 voltage pulses), and the relative standard deviation of Gmax/Gmin is only 2.9%. Leveraging these superior reliability and performance attributes, we propose a neuromorphic sensory system for finger motion tracking and hand gesture recognition as a potential elemental technology for the metaverse. This system consists of a stretchable double-layered photoacoustic strain sensor and a crossbar array neural network. We perform training and recognition tasks on ultrasonic patterns associated with finger motion and hand gestures, attaining a recognition accuracy of 97.9% and 97.4%, comparable to that of SW-NN (99.8% and 98.7%).

CNN neural network temporal feature storage structure fusion for the visible channel equalization algorithm

The visible light communication channel has time-varying characteristics and is difficult to predict. This paper proposes an equalization algorithm based on the structure of a convolutional neural network (CNN), combining time series feature length and long short-term memory (LSTM), and adding a residual structure. It can be seen that the equalization coefficient vector of the optical channel is a time series, which can reflect the noise characteristics of the channel and has storage characteristics. The equalizer algorithm can accurately learn the complex channel characteristics and calculate the compensation coefficient according to the channel characteristics. It can also restore the original transmission signal. At the same time, this paper also examines the compensation method of the receiver in the mobile state. The long-term memory parameters of LSTM are used to represent the sequence causality in the memory channel, and CNN and residual structure are used to refine the results and improve the accuracy of the reconstruction. The simulation results show that the algorithm can effectively eliminate the influence of the fading characteristics of the visible optical channel, improve the bit error rate performance of system transmission, solve the overall problem of channel corruption, and precisely restore the original transmission signal with fast convergence speed. In addition, this method can achieve a better balance between performance and complexity compared to the traditional contention balancing method, which proves the potential and effectiveness of the proposed channel balancing method.

The Danger of Minimum Exposures: Understanding Cross-App Information Leaks on iOS through Multi-Side-Channel Learning.

Research on side-channel leaks has long been focusing on the information exposure from a single channel (memory, network traffic, power, etc.). Less studied is the risk of learning from multiple side channels related to a target activity (e.g., website visits) even when individual channels are not informative enough for an effective attack. Although the prior research made the first step on this direction, inferring the operations of foreground apps on iOS from a set of global statistics, still less clear are how to determine the maximum information leaks from all target-related side channels on a system, what can be learnt about the target from such leaks and most importantly, how to control information leaks from the whole system, not just from an individual channel. To answer these fundamental questions, we performed the first systematic study on multi-channel inference, focusing on iOS as the first step. Our research is based upon a novel attack technique, called Mischief, which given a set of potential side channels related to a target activity (e.g., foreground apps), utilizes probabilistic search to approximate an optimal subset of the channels exposing most information, as measured by Merit Score, a metric for correlation-based feature selection. On such an optimal subset, an inference attack is modeled as a multivariate time series classification problem, so the state-of-the-art deep-learning based solution, InceptionTime in particular, can be applied to achieve the best possible outcome. Mischief is found to work effectively on today's iOS (16.2), identifying foreground apps, website visits, sensitive IoT operations (e.g., opening the door) with a high confidence, even in an open-world scenario, which demonstrates that the protection Apple puts in place against the known attack is inadequate. Also importantly, this new understanding enables us to develop more comprehensive protection, which could elevate today's side-channel research from suppressing leaks from individual channels to controlling information exposure across the whole system.

Dynamic Thermal Management of 3D Memory through Rotating Low Power States and Partial Channel Closure

Modern high-performance and high-bandwidth three-dimensional (3D) memories are characterized by frequent heating. Prior art suggests turning off hot channels and migrating data to the background DDR memory, incurring significant performance and energy overheads. We propose three Dynamic Thermal Management (DTM) approaches for 3D memories, reducing these overheads. The first approach, Rotating-channel Low-power-state-based DTM (RL-DTM) , minimizes the energy overheads by avoiding data migration. RL-DTM places 3D memory channels into low power states instead of turning them off. Since data accesses are disallowed during low power state, RL-DTM balances each channel’s low-power-state duration. The second approach, Masked rotating-channel Low-power-state-based DTM (ML-DTM) , is a fine-grained policy that minimizes the energy-delay product (EDP) and improves the performance of RL-DTM by considering the channel access rate. The third strategy, Partial channel closure and ML-DTM , minimizes performance overheads of existing channel-level turn-off-based policies by closing a channel only partially and integrating ML-DTM, reducing the number of channels being turned off. We evaluate the proposed DTM policies using various mixes of SPEC benchmarks and multi-threaded workloads and observe them to significantly improve performance, energy, and EDP over state-of-the-art approaches for different 3D memory architectures.

Postponing the decay of entanglement and quantum coherence for maximally entangled mixed states under the action of correlated noise channels

We investigate the dynamics of a maximally entangled mixed state (MEMS) under the action of correlated noise channels. The channel acts in a way that its successive uses are correlated. We have studied the MEMS properties, including quantum coherence and entanglement. For partially correlated channels, both the entanglement and coherence of MEMS are found to decay much slower than those of the memoryless channels. Moreover, we observe a freezing effect of coherence for phase damping as well as depolarizing channels and freezing of entanglement for phase-damping channels with perfect memory. For amplitude damping and depolarizing channels, memory helps in either delaying the sudden death of entanglement or slowing the decay rate of coherence. These observations suggest that memory channels perform better than memoryless channels in maintaining the integrity of quantum states and have utility in quantum information processing protocols.

Interleaved LDPC Decoding Scheme Improves 3-D TLC NAND Flash Memory System Performance

Although NAND flash memory does a lot of work in effectively using Error Correcting Code(ECC) to reduce Uncorrectable Bit Error Rate(UBER). However, if the Frame Error Rate(FER) is not reduced, the lower UBER cannot effectively reduce the read latency of the flash memory system. This phenomenon is especially evident at the end of the flash memory lifetime, where conventional methods significantly reduce the UBER but not to zero, and the remaining error bits are still evenly distributed throughout the flash memory page, resulting in a significant increase in read latency. In this paper, an interleaved LDPC decoding scheme is proposed. By re-evaluating the flash memory channel during the decoding process, the codewords in the flash memory page are corrected frame by frame, and the problem of high FER is solved at the end of the flash memory lifetime. Compared with the conventional algorithm, the proposed method can reduce the FER by up to 34%, reduce the average decoding iterations by 63.4%, and reduce the read latency by up to 65%.

Textile defect detection based on multi‐proportion spatial attention mechanism and channel memory feature fusion network

AbstractThis paper presents a textile defect detection method that utilizes a multi‐proportion spatial attention mechanism and channel memory feature fusion network by addressing the difficulties presented by complicated shapes and large size variations. In particular, a multi‐proportion spatial attention mechanism (MPAM) is introduced, which employs multi‐proportion convolution to improve the backbone network's capacity to detect non‐uniform structural defects. Additionally, the generality and adaptability of the model are enhanced by a multi‐scale spatial pyramid pooling structure (MS‐SPP). Second, a channel attention mechanism‐based memory feature fusion network is developed, which incorporates channel attention to adaptive weight the feature channels, focusing on crucial information channels to efficiently fuse contextual features and enhance the model's memory capacity. Finally, a novel efficient Wise‐IoU (EWIoU) loss function is proposed, which utilizes a dynamic non‐monotonic focusing mechanism to increase the penalty on distance measurement, thus enhancing the model's detection performance. Experiment findings on the ZJU‐Leaper and Tianchi textile datasets reveal that compared to the YOLOv7 baseline, the method in this paper has an increase of 6.5 and 2 percentage points, respectively, and the detection accuracy is better than most existing networks.

Performance of entanglement-assisted quantum codes with noisy ebits over asymmetric and memory channels

Entanglement-assisted quantum error correction codes (EAQECCs) play an important role in quantum communications with noise. Such a scheme can use arbitrary classical linear code to transmit qubits over noisy quantum channels by consuming some ebits between the sender (Alice) and the receiver (Bob). It is usually assumed that the preshared ebits of Bob are error free. However, noise on these ebits is unavoidable in many cases. In this work, we evaluate the performance of EAQECCs with noisy ebits over asymmetric quantum channels and quantum channels with memory by computing the exact entanglement fidelity of several EAQECCs. We consider asymmetric errors in both qubits and ebits and show that the performance of EAQECCs in entanglement fidelity gets improved for qubits and ebits over asymmetric channels. In quantum memory channels, we compute the entanglement fidelity of several EAQECCs over Markovian quantum memory channels and show that the performance of EAQECCs is lowered down by the channel memory. Furthermore, we show that the performance of EAQECCs is diverse when the error probabilities of qubits and ebits are different. In both asymmetric and memory quantum channels, we show that the performance of EAQECCs is improved largely when the error probability of ebits is reasonably smaller than that of qubits.

Bi‐directional LSTM based channel estimation in 5G massive MIMO OFDM systems over TDL‐C model with Rayleigh fading distribution

SummaryIn this work, a deep learning (DL)‐based massive multiple‐input multiple‐output (mMIMO) orthogonal frequency division multiplexing (OFDM) system is investigated over the tapped delay line type C (TDL‐C) model with a Rayleigh fading distribution at frequencies ranging from 0.5 to 100 GHz. The proposed bi‐directional long short‐term memory (Bi‐LSTM) channel state information (CSI) estimator uses online learning during training and offline learning during the practical implementation phase. The design of the estimator takes into account situations in which prior knowledge of channel statistics is limited and targets excellent performance, even with limited pilot symbols (PS). Three separate loss functions (mean square logarithmic error [MSLE], Huber, and Kullback–Leibler Distance [KLD]) are assessed in three classification layers. The symbol error rate (SER) and outage probability performance of the proposed estimator are evaluated using a number of optimization techniques, such as stochastic gradient descent (SGD), momentum, and the adaptive gradient (AdaGrad) algorithm. The Bi‐LSTM‐based CSI estimator is trained considering a specific number of PS. It can be readily seen that by incorporating a cyclic prefix (CP), the system becomes more resilient to channel impairments, resulting in a lower SER. Simulations show that the SGD optimization approach and Huber loss function‐trained Bi‐LSTM‐based CSI estimator have the lowest SER and very high estimation accuracy. By using deep neural networks (DNNs), the Bi‐LSTM method for CSI estimation achieves a superior channel capacity (in bps/Hz) at 10 dB than long short‐term memory (LSTM) and other conventional CSI estimators, such as minimum mean square error (MMSE) and least squares (LS). The simulation results validate the analytical results in the study.

Rate-distortion theory of neural coding and its implications for working memory.

Rate-distortion theory provides a powerful framework for understanding the nature of human memory by formalizing the relationship between information rate (the average number of bits per stimulus transmitted across the memory channel) and distortion (the cost of memory errors). Here we show how this abstract computational-level framework can be realized by a model of neural population coding. The model reproduces key regularities of visual working memory, including some that were not previously explained by population coding models. We verify a novel prediction of the model by reanalyzing recordings of monkey prefrontal neurons during an oculomotor delayed response task.

Lightweight Channel Codes for ISI Mitigation in Molecular Communication Between Bionanosensors

Channel memory and inter-symbol interference (ISI) are harmful factors in diffusion-based molecular communication (DBMC) between bionanosensors. To tackle these problems, this paper proposes a lightweight ISI-mitigating coding scheme to improve the system performance by shaping the signal using a constrained code. To characterize the proposed coding scheme theoretically, we derive analytical expressions for the bit error rate (BER) and the achievable rate based on Central Limit Theorem. Computer simulations are conducted to verify the accuracy of the theoretical results and demonstrate the superiority of the proposed coding scheme compared with the existing coding schemes.

Noncryogenic Quantum Repeaters with hot Hybrid Alkali-Noble Gases

We propose a quantum repeater architecture that can operate without cryogenics. Each node in our architecture builds on a cell of hot alkali atoms and noble-gas spins that offer an hours-long storage time. Such a cell of hybrid gases is placed in a ring cavity, which allows us to suppress the detrimental four-wave-mixing noise in the system. We investigate the protocol based on a single-photon source made of an ensemble of the same hot alkali atoms. A single photon emitted from the source is either stored in the memory or transmitted to the central station to be detected. We quantify the fidelity and success probability of generating entanglement between two remote ensembles of noble-gas spins by taking into account finite memory efficiency, channel loss, and dark counts in detectors. We describe how the entanglement can be extended to long distances via entanglement swapping operations by retrieving the stored signal. Moreover, we quantify the performance of this proposed repeater architecture in terms of repeater rates and overall entanglement fidelities and compare it to another recently proposed noncryogenic quantum repeater architecture based on nitrogen-vacancy (N-V) centers and optomechanical spin-photon interfaces. As the system requires a relatively simple setup, it is easier to perform multiplexing, which enables achieving rates comparable to the rates of repeaters with N-V centers and optomechanics, while the overall entanglement fidelities of the present scheme are higher than the fidelities of the previous scheme. Our work shows that a scalable long-distance quantum network made of hot hybrid atomic gases is within reach of current technological capabilities.

Effective switchless inter-FPGA memory networks

FPGA network bandwidth increases to 400 Gbps or higher by high-density optical integration, e.g., Co-Packaged Optics (CPO) and onboard Si-photonics transceivers. This study presents its lightweight switchless network architecture by exploiting an indirect path, consisting of up to k one-hop paths for a diameter-k network topology. It integrates interconnection network and memory channels into a lightweight memory-to-memory inter-FPGA network. It has a uni-directional network topology for connecting the largest number of FPGAs. A newly introduced indirect flow control enables lossless uni-directional networks. We present multi-path point-to-point communications, multi-port message-combine collective communications, and efficient WDM (Wavelength Division Multiplexing) cabling method for the uni-directional networks. We implement and evaluate the uni-directional switchless network on an FPGA cluster consisting of six custom Stratix10 MX2100 FPGA cards connected with a 16-wavelength Arrayed Waveguide Grating (AWG) router. Simulation results show that the multi-port message-combine collective communication achieves 7× faster than conventional communication with 272 FPGAs. Effective WDM cabling using 16 wavelengths decreases the number of cables by 88% compared with parallel optical cabling for 272 FPGAs.

Two types of redundancy in multimedia learning: a literature review.

Regarding the redundancy effect in multimedia learning environments, more consistency is needed in the theoretical assumptions and investigation of this effect. Current research lacks a comprehensive account of different redundant scenarios in which materials facilitate or inhibit learning and provides little conceptual guidance on how learning processes are affected by different types of redundancy. Theoretical assumptions refer to redundancy as a contentual overlap of information provided by the learning material; in this case, processing duplicated information strains the learners' limited cognitive capacities. Other assumptions refer to the role of processing limitations in working memory channels, including separate processing for visual and verbal information. In this case, an ineffective combination of sources leads to an overload of the limited working memory capacity. This paper reviews empirical research on the redundancy effect (63 studies) and classifies two types of redundancy: (1) content redundancy, and (2) working memory channel redundancy. From an instructional psychology perspective, the analyses reveal four different implementations of redundant scenarios: (1) adding narration to visualizations, (2) adding written text to visualizations, (3) adding written text to narration, and (4) adding written text to narrated visualizations. Regarding the effects of the two redundancy types within these scenarios, analyses indicate positive effects of content redundancy (affected by learners' prior knowledge), negative effects of working memory channel redundancy (regarding visualizations and written text), and positive effects of working memory channel redundancy (regarding narration and written text). Moreover, results point to factors that might moderate the effect of redundancy and illustrate interactions with existing multimedia effects. Overall, this review provides an overview of the state of empirical research and reveals that the consideration of both redundancy types provides further explanations in this field of research.

Maximum Achievable Rate of Resistive Random-Access Memory Channels by Mutual Information Spectrum Analysis

The maximum achievable rate is derived for resistive random-access memory (ReRAM) channel with sneak-path interference. Based on the mutual information spectrum analysis, the maximum achievable rate of ReRAM channel with independent and identically distributed (i.i.d.) binary inputs is derived as an explicit function of channel parameters such as the distribution of cell selector failures and channel noise level. Due to the randomness of cell selector failures, the ReRAM channel demonstrates multi-status characteristic. For each status, it is shown that as the array size is large, the fraction of cells affected by sneak paths approaches a constant value. Therefore, the mutual information spectrum of the ReRAM channel is formulated as a mixture of multiple stationary channels. Maximum achievable rates of the ReRAM channel with different settings, such as single- and across-array codings, with and without data shaping, and optimal and treating-interference-as-noise (TIN) decodings, are compared. These results provide valuable insights on the code design for ReRAM.

Deep Reinforcement Learning for Latency-Sensitive Communication With Adaptive Redundant Retransmissions

This paper studies packet repetition strategies over erasure channels with memory and a long feedback delay. The problem is initially formulated as a communications problem where a source wishes to transmit one message packet to a destination while minimizing both the delay and the number of transmissions. At each time instant, the sender is provided a delayed acknowledgement feedback about past attempts, and must decide whether to attempt a new transmission or not. This problem is then re-formulated as an episodic reinforcement learning problem, where an agent attempts to learn the optimal transmission policy, provided delayed feedback about past transmission attempts. The agent is helped by a channel estimator, which attempts to capture the channel memory and use that to predict probabilities of erasures in a future window. This channel estimator is also data-driven and learns the channel model without any <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> channel knowledge. The paper presents a lower bound on the achievable trade-off between delay and number of transmissions for any channel modeled as a Markov process. Experimental results show that the combination of the proposed channel estimator and the agent can noticeably outperform naive strategies for channels with memory, and achieves results close to the lower bound.

On Two Sensors Scheduling for Remote State Estimation With a Shared Memory Channel in a Cyber-Physical System Environment.

This article studies two sensors scheduling with a shared memory channel for remote state estimation in cyber-physical systems (CPSs). We consider that each sensor monitors a plant and sends its local estimate to the remote estimator over a shared memory communication channel, of which the packet reception results between two successive time instants are correlated. This article focuses on how the two sensors are scheduled to minimize the total estimation errors at the remote side. The problem is formulated as the Markov decision process (MDP) and the optimal policy is derived. Moreover, the threshold structure of the optimal policy is given to reduce computation overhead. After proving the Whittle indexability of the overall system under a given condition, the Whittle index policy is adopted to further reduce the computation overhead. Numerical simulations are given to illustrate the theoretical results.

Over-the-Air Split Machine Learning in Wireless MIMO Networks

In split machine learning (ML), different partitions of a neural network (NN) are executed by different computing nodes, requiring a large amount of communication cost. As over-the-air computation (OAC) can efficiently implement all or part of the computation at the same time of communication, thus by substituting the wireless transmission in the traditional split ML framework with OAC, the communication load can be eased. In this paper, we propose to deploy split ML in a wireless multiple-input multiple-output (MIMO) communication network utilizing the intricate interplay between MIMO-based OAC and NN. The basic procedure of the OAC split ML system is first provided, and we show that the inter-layer connection in a NN of any size can be mathematically decomposed into a set of linear precoding and combining transformations over a MIMO channel carrying out multi-stream analog communication. The precoding and combining matrices which are regarded as trainable parameters, and the MIMO channel matrix, which are regarded as unknown (implicit) parameters, jointly serve as a fully connected layer of the NN. Most interestingly, the channel estimation procedure can be eliminated by exploiting the MIMO channel reciprocity of the forward and backward propagation, thus greatly saving the system costs and/or further improving its overall efficiency. The generalization of the proposed scheme to the conventional NNs is also introduced, i.e., the widely used convolutional NNs. We demonstrate its effectiveness under both the static and quasi-static memory channel conditions with comprehensive simulations.

Sustainability of entanglement sudden death under the action of memory channel

Quantum entanglement is an important resource in quantum information science. Its useful effects get disturbed when it is subjected to environmental effect and led to decay of entanglement. The behaviour of entanglement have been studied for maximally entangled mixed state (MEMS) under different cases. Here we proposed a method for the sustainability of entanglement using the concept of memory noise. During this study interesting results were observed for the intensification of entanglement sudden death (ESD) which have many applications in quantum information processing task. Analytical results were derived for MEMS state. After detailed discussion the problem is theoretically analysed and simulation results of ESD are presented.

Usage of LDPC Codes in a Gilbert Channel

Although low-density parity-check (LDPC) codes in modern communication standards have been extensively studied over a memoryless channel, their burst error correction capacity in channels with memory has yet to be thoroughly analyzed. The conventional approach to transmission in channels with memory uses interleaving within a buffer of several codewords. However, such an approach reduces the efficiency of the redundancy embedded by the error-correcting code. It is known from information theory that considering channel memory during decoding allows the transmission rate to be increased. An evaluation of the decoding error probability of different types of low-density parity-check codes in channels with memory is presented along with estimates of minimum distance and burst error correction capability for the considered codes. The decoding error probability is estimated for conventional decoding with deinterleaving and decoding taking channel memory into account. The decoding error probability is estimated for several parameters of a channel with memory and different buffer lengths. The obtained results reveal the absence of the unique decoding approach for all parameters of the channel with memory. The best decoding error probability is determined by the degree of channel memory correlation.