Related papers: Computational Associative Memory with Amorphous In…
THz band enabled large scale massive MIMO (M-MIMO) is considered as a key enabler for the 6G technology, given its enormous bandwidth and for its low latency connectivity. In the large-scale M-MIMO configuration, enlarged array aperture and…
Using 2-D self-consistent ballistic quantum transport simulations, we investigate the short-channel behavior of graphene field-effect transistors and its impact on the device transconductance and subsequently the intrinsic cut-off frequency…
3D Gaussian splatting (3DGS) has recently emerged as an alternative representation that leverages a 3D Gaussian-based representation and introduces an approximated volumetric rendering, achieving very fast rendering speed and promising…
As data-centric computing advances, energy-efficient interconnects are increasingly critical for AI-driven systems. Traditional metal conductors face severe limitations at nanoscale due to increased resistivity from surface scattering. In…
Most advances in 3D Generative Adversarial Networks (3D GANs) largely depend on ray casting-based volume rendering, which incurs demanding rendering costs. One promising alternative is rasterization-based 3D Gaussian Splatting (3D-GS),…
Conical microfluidic channels filled with electrolytes exhibit volatile memristive behavior, offering a promising platform for energy-efficient, neuromorphic computing. Here, we integrate these iontronic channels as additional nonlinear…
Oxidation of two-dimensional (2D) layered materials has proven advantageous in creating oxide/2D material heterostructures, opening the door for a new paradigm of low-power electronic devices. Gallium (II) sulfide ($\beta$-GaS), a hexagonal…
The rapid growth of artificial intelligence (AI) and data-centric computing is driving exabyte-scale data transfer, pushing conventional interconnect technologies toward fundamental bandwidth and energy limits. Although optical…
The rapid growth of digital technology has driven the need for efficient storage solutions, positioning memristors as promising candidates for next-generation non-volatile memory (NVM) due to their superior electrical properties. Organic…
This work presents the design and analysis of a sixth-order triple-coupled transformer-based tank, enabling third-harmonic expansion for mmWave VCOs. Unlike conventional fourth-order tanks, the proposed tank inherently supports three…
Resistive random-access memories, also known as memristors, whose resistance can be modulated by the electrically driven formation and disruption of conductive filaments within an insulator, are promising candidates for neuromorphic…
The torrential influx of floating-point data from domains like IoT and HPC necessitates high-performance lossless compression to mitigate storage costs while preserving absolute data fidelity. Leveraging GPU parallelism for this task…
This paper introduces channel gating, a dynamic, fine-grained, and hardware-efficient pruning scheme to reduce the computation cost for convolutional neural networks (CNNs). Channel gating identifies regions in the features that contribute…
We investigated the microscopic and electronic structures amorphous oxide semiconductors InGaZnO$_{4}$ (a-IGZO) and the role of O-deficiency through the first-principle calculations. The structure of the amorphous oxide is complicated by…
Memristors that mimic brain functions are crucial for energy-efficient neuromorphic devices. Ion channels that emulate biological synapses are still in the early stages of development, especially the tunability of memory states. Here, we…
Integrated photonic quantum memories are essential components for scalable quantum networks and photonic information processors. However, prior implementations have been confined to single-channel operation, limiting their capacity to…
We study sequential tunneling of magnetic excitations in nonitinerant systems (either magnons or spinons) through triangular molecular magnets. It is known that the quantum state of such molecular magnets can be controlled by application of…
Deep learning hardware designs have been bottlenecked by conventional memories such as SRAM due to density, leakage and parallel computing challenges. Resistive devices can address the density and volatility issues, but have been limited by…
Atomically thin two-dimensional (2D) materials are promising candidates for sub-10 nm transistor channels due to their ultrathin body thickness, which results in strong electrostatic gate control. Properly scaling a transistor technology…
Using accurate dissipative DFT-NEGF atomistic-simulation techniques within the Wannier-Function formalism, we give a fresh look at the possibility of sub-10 nm scaling for high-performance CMOS applications. We show that a combination of…