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Related Articles from SNS
Lossy Microwave Linear Analog Computer (MiLAC) for Future MIMO: Learning-based Architecture Designs for Spectral and Energy Efficiency Maximization
arXiv:2606.02369v1 Announce Type: cross Abstract: Microwave linear analog computers (MiLACs) offer a transformative paradigm for future multiple-input multiple-output (MIMO) systems by shifting complex signal processing into the analog domain, thereby significantly reducing computational complexity, radio-frequency chains, and analog-digital converters, while speeding up computation. However, the practical deployment of MiLACs is severely constrained by the inherent hardware losses of the...
Microwave Linear Analog Computer (MiLAC) for Simultaneous Active and Passive Beamforming
arXiv:2605.31549v1 Announce Type: new Abstract: Microwave linear analog computers (MiLACs) have recently emerged to enable high-performance and efficient beamforming in the analog domain. In this paper, we introduce a dual-functionality framework for MiLAC-aided transceivers. Beyond analog-domain precoding/combining (active beamforming), a MiLAC and its antenna array can simultaneously act as a reconfigurable intelligent surface (RIS) (passive beamforming).
Heterogeneous Mapping for Analog In-Memory Computing Accelerators: A Unified Workflow
arXiv:2606.02672v1 Announce Type: new Abstract: Analog In-Memory Computing (AIMC) accelerators execute matrix-vector multiplications directly within memory arrays, reducing data movement and improving DNN inference efficiency. Their limited effective precision motivates heterogeneous architectures that combine analog compute tiles with digital processing units. This letter classifies existing methods for partitioning DNN workloads across these resources by mapping granularity, optimization...
Reinforcement Learning-Enabled Agent for Transmitter Optimization in Digital-Analog Radio-over-Fiber Fronthaul
arXiv:2606.04840v1 Announce Type: new Abstract: Digital-analog radio-over-fiber (DA-RoF) has emerged as a promising fronthaul solution that combines the high spectral efficiency of analog transmission with the robustness of digital transmission. However, the performance of DA-RoF critically depends on several tightly coupled parameters, including the rounding factor (RF), scaling factor (SF), geometric shaping (GS) factor, and pre-equalization taps coefficients, which jointly affect...
Photonic Analog Quantum Simulation of (1+1)-Dimensional $U(1)$ Lattice Gauge Theory with Dynamical Matter
Announce Type: cross Abstract: We propose a photonic scheme for analog quantum simulation of a $U(1)$ Lattice Gauge Theory (LGT) with dynamical matter based on the Jaynes-Cummings-Hubbard (JCH) model. Here, an array of interacting cavities in the strong-coupling regime of cavity Quantum Electrodynamics is mapped onto the alternating matter and gauge-field sites of the spin-1/2 Quantum Link Model. In contrast to other analog LGT quantum simulation methods, our approach implements the desired...
Beyond Gradient Descent: Adam for Analog Ising Machines
Announce Type: new Abstract: As Moore's law reaches its limits, Ising machines offer a promising alternative computing approach for difficult optimization problems. However, many analog, time-continuous Ising machines rely on gradient-descent-like dynamics to find solutions, which can limit speed and robustness.
Investigating Energy Bounds of Analog Compute-in-Memory with Local Normalization
arXiv:2602.08081v2 Announce Type: replace Abstract: Modern edge AI workloads demand maximum energy efficiency, motivating the pursuit of analog Compute-in-Memory (CIM) architectures. Simultaneously, the popularity of Large-Language-Models (LLMs) drives the adoption of low-bit floating-point formats which prioritize dynamic range. However, the conventional direct-accumulation CIM accommodates floating-points by normalizing them to a shared widened fixed-point scale.
GraphWalker: Patient Analogy Meets Information Gain for Clinical Reasoning with Large Language Models
Announce Type: replace Abstract: Clinical reasoning over electronic health records (EHRs) is a fundamental yet challenging task in modern healthcare. While large language models (LLMs) offer a promising paradigm via in-context demonstrations that requires no task-specific parameter updates, existing methods for reasoning by patient analogy in EHR settings suffer from three core limitations: (1) Perspective Limitation, where data-driven similarity misaligns with LLM reasoning needs while...
Beyond Gradient Descent: Adam for Analog Ising Machines
Announce Type: cross Abstract: As Moore's law reaches its limits, Ising machines offer a promising alternative computing approach for difficult optimization problems. However, many analog, time-continuous Ising machines rely on gradient-descent-like dynamics to find solutions, which can limit speed and robustness.
Bounds for Single-Error-Correcting Analog Codes
arXiv:2606.03011v1 Announce Type: new Abstract: We study single-error correction for analog codes over $\mathbb{R}$. A key performance measure is the parameter $\Gamma_2(\mathcal{C})$, which quantifies the minimum separation required between large outlying errors that need to be located/corrected and bounded tolerable perturbations. We prove that every real linear $[n,n-2]$ code $\mathcal{C}$ satisfies \[ \Gamma_2(\mathcal{C})\ge \frac{1}{\sin^2(\pi/2n)}. This resolves Roth's open problem on...