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As the demand for efficient, low-power computing in embedded and edge devices grows, traditional computing methods are becoming less effective for handling complex tasks. Stochastic computing (SC) offers a promising alternative by…
Multiple transient memories, originally discovered in charge-density-wave conductors, are a remarkable and initially counterintuitive example of how a system can store information about its driving. In this class of memories, a system can…
Non-orthogonal multiple access (NOMA) and spectrum sharing (SS) are two emerging multiple access technologies for efficient spectrum utilization in the fifth-generation (5G) wireless communications standard. In this paper, we present a…
Tensor parallelism (TP) in large-scale LLM inference and training introduces frequent collective operations that dominate inter-GPU communication. While in-switch computing, exemplified by NVLink SHARP (NVLS), accelerates collective…
Current-driven switching of nonvolatile spintronic materials and devices based on spin-orbit torques offer fast data processing speed, low power consumption, and unlimited endurance for future information processing applications. Analogous…
We propose a simple model of a nanoswitch as a memory resistor. The resistance of the nanoswitch is determined by electron tunneling through a nanoparticle diffusing around one or more potential minima located between the electrodes in the…
Superconductor electronics (SCE) is a promising complementary and beyond CMOS technology. However, despite its practical benefits, the realization of SCE logic faces a significant challenge due to the absence of dense and scalable…
Nanoscale resistive switching devices (memristive devices or memristors) have been studied for a number of applications ranging from non-volatile memory, logic to neuromorphic systems. However a major challenge is to address the potentially…
Resistive switching devices, important for emerging memory and neuromorphic applications, face significant challenges related to control of delicate filamentary states in the oxide material. As a device switches, its rapid conductivity…
The current induced magnetization reversal in nanoscale spin valves is a potential alternative to magnetic field switching in magnetic memory devices. We show that the critical switching current can be decreased by an order of magnitude by…
Resistive Random Access Memory (ReRAM) is a promising candidate for implementing Computing-in-Memory (CIM) architectures and neuromorphic circuits. ReRAM cells exhibit significant variability across different memristive devices and cycles,…
Emerging non-volatile memories (NVMs) have currently attracted great interest for their potential applications in advanced low-power information storage and processing technologies. Conventional NVMs, such as magnetic random access memory…
Optical interconnects are becoming a major bottleneck in scaling up future GPU racks and network switches within data centers. Although 200 Gb/s optical transceivers using PAM-4 modulation have been demonstrated, achieving higher data rates…
The intrinsically rectifying-resistive switching (IR-RS) has been regarded as an effective way to address the crosstalk issue, due to the Schottky diodes formed at the metal/oxide interfaces in the ON states to suppress the sneak current at…
Conversational recommender systems (CRSs) are designed to suggest the target item that the user is likely to prefer through multi-turn conversations. Recent studies stress that capturing sentiments in user conversations improves…
This work proposes a switched model reference adaptive control (S-MRAC) architecture for a multi-input multi-output (MIMO) switched linear system with memory for enhanced learning. A salient feature of the proposed method that separates it…
In-memory computing is a promising alternative to traditional computer designs, as it helps overcome performance limits caused by the separation of memory and processing units. However, many current approaches struggle with unreliable…
Resistive random access memory (RRAM) is very well known for its potential application in in-memory and neural computing. However, they often have different types of device-to-device and cycle-to-cycle variability. This makes it harder to…
Reservoir Computing (RC) is a bio-inspired machine learning framework, and various models have been proposed. RC is a well-suited model for time series data processing, but there is a trade-off between memory capacity and nonlinearity. In…
There is an extremely high demand for a high speed, low power, low leakage, and low noise Static Random-Access Memory (SRAM) for high performance cache memories. The energy efficiency of SRAM is of paramount importance in both high…