Abstract

This paper presents a dual-loop reference-less Clock and Data Recovery (CDR) architecture implemented in TSMC 28 nm CMOS technology for PCIe Gen3 applications. The proposed design features an extended BiDirectional Frequency Detector (BFD) that enables rapid frequency acquisition in both directions over a wide locking range. By utilizing DNFAST and DNSLOW signals, the BFD dynamically adjusts the correction rate based on frequency error, ensuring accurate frequency tracking and a seamless transition from frequency acquisition to phase tracking. This approach significantly enhances the locking performance and stability of the CDR system. The proposed CDR achieves a locking time of 4.1μs when transitioning from PCIe Gen1 (2. 5 G b p s) to PCIe Gen3 (8 G b p s) under a PRBS7 pattern with a power consumption of 3.9 mW at 0.9 V power supply.

Abstract

In this paper, we propose an efficient and robust framework for estimating leakage power while considering statistical variations in process parameters, supply voltage, and temperature (PVT) in FinFET-based digital circuits. Extensive testing reports an average error of less than 1 % in the ML models for standard digital cells, with an average MAPE (Mean Absolute Percentage Error) of 0.764%,0.770%, and 0.814% in the 7 nm,10 nm, and 16 nm FinFET technology nodes, respectively, which ensures a highly accurate leakage estimate for larger circuits. Additionally, we investigated the transistor-level approach, which distinctively estimates subthreshold, gate, and body leakage power using only transistor-level models and stacks, and investigated its trade-offs to gate-level models. Using ML techniques, we achieve a speedup of over 400 times compared to HSPICE for the characterization of digital blocks, using which complex cell designs can be analyzed across various input test combinations, significantly saving the time required for extensive simulations.

Abstract

Document layout analysis is essential for downstream tasks such as information retrieval, extraction, OCR, and digitization. However, existing large-scale datasets like PubLayNet and DocBank lack fine-grained region labels and multilingual diversity, making them insufficient for representing complex document layouts. Human-annotated datasets such as M6Doc and D4LA offer richer labels and greater domain diversity, but are too small to train robust models and lack adequate multilingual coverage. This gap is especially pronounced for Indic documents, which encompass diverse scripts yet remain underrepresented in current datasets, further limiting progress in this space. To address these shortcomings, we introduce IndicDLP, a large-scale foundational document layout dataset spanning 11 representative Indic languages alongside English and 12 common document domains. Additionally, we curate UED-mini, a dataset derived from DocLayNet and M6Doc, to enhance pretraining and provide a solid foundation for Indic layout models. Our experiments demonstrate that fine-tuning existing English models on IndicDLP significantly boosts performance, validating its effectiveness. Moreover, models trained on IndicDLP generalize well beyond Indic layouts, making it a valuable resource for document digitization. This work bridges gaps in scale, diversity, and annotation granularity, driving inclusive and efficient document understanding

Abstract

A 16-Gb/s continuous-time linear equalizer (CTLE) is designed in 28-nm CMOS technology, featuring an active inductor load and feedforward compensation to enhance high-frequency performance. The proposed design features a power-efficient architecture, achieving a remarkable eye opening of 45.75 ps and a vertical differential eye opening of 781.19 mV. The design achieves reliable operation for channel losses of 9 dB, consuming a total power of 3.3 mW from a 0.9 V supply. This CTLE provides an energy-efficient solution for high-speed SerDes applications, with a calculated figure-of-merit of 0.022 pJ/hit/dB.

Abstract

Segment matching is an important intermediate task in computer vision that establishes correspondences between semantically or geometrically coherent regions across images. Unlike keypoint matching, which focuses on localized features, segment matching captures structured regions, offering greater robustness to occlusions, lighting variations, and viewpoint changes. In this paper, we leverage the spatial understanding of 3D foundation models to tackle wide-baseline segment matching, a challenging setting involving extreme viewpoint shifts. We propose an architecture that uses the inductive bias of these 3D foundation models to match segments across image pairs with up to 180 degree view-point change rotation. Extensive experiments show that our approach outperforms state-of-the-art methods, including the SAM2 video propagator and local feature matching methods, by up to 30% on the AUPRC metric, on ScanNet++ and Replica datasets. We further demonstrate benefits of the proposed model on relevant downstream tasks, including 3D instance mapping and object-relative navigation. Project Page: https://segmast3r.github.io/

Abstract

We propose an alignment-free, end-to-end Non-Audible Murmur (NAM)-to-Speech conversion model. Existing methods rely on large NAM-text pairs per speaker to generate high-quality alignments for training non-autoregressive models. However, alignment quality deteriorates when trained on multispeaker data, limiting their ability to generalize and effectively utilize the available training data. To address this, we introduce a streamlined autoregressive approach that eliminates the need for explicit alignment learning. By leveraging multi-speaker samples, synthetic training pairs, and multitask character recognition training, our method reduces the word error rate (WER) by 59.19% compared to the state-of-the-art (SOTA) on two public datasets. We demonstrate the model’s zero-shot capability and validate the effectiveness of multitask training through ablation studies. Speech samples are available at https: //noalignnam.github.io/autoregressiveNAM/

Abstract

We use the spin-boson model to describe the dynamics of a two-level atom interacting with Fabry-Pérot cavity modes. We solve the Schrödinger equation for the system-bath model without the Born-Markov approximation to derive the non-Markovian reduced dynamics of the qubit. We further construct an exact Lindblad-type master equation for it. Similar to the quantum Otto cycle, we construct a non-Markovian quasicyclic process based on the atom-cavity interactions, which we call the quasi-Otto cycle. For judicious choices of input state and parameters, the quasicycle can be more efficient as a quantum engine than the Otto cycle. We also show that if the quasicycle is repeated multiple times, the efficiency of the quasi-Otto engine asymptotically approaches that of the Otto engine.

Abstract

Water scarcity and inefficient resource management present critical challenges, with conventional flow measurement techniques often relying on expensive and invasive methods. This work evaluates the performance of three low-cost pressure sensors (HK3022, MBS3000, and Series 21Y) for internet of things (IoT) and machine learning (ML) based water flow estimation. Tests were conducted in a closed-loop experimental setup across pressure ranges from 1.0 to 2.0 bar, generating over 8,640 pressure readings and 2,160 flow measurements. Sensor performance was assessed through pressure measurement accuracy metrics (RMSE and CV), and flow estimation reliability was assessed using five ML algorithms. Results demonstrate that all sensors achieve comparable flow estimation accuracy. While calibration improves absolute pressure measurement accuracy, it provides minimal advantage for flow estimation tasks. This low-cost, non-invasive approach offers a promising water monitoring solution that is accessible and economically viable.

Abstract

We present a unified approach for constraint displacement problems in which a robot finds a feasible path by displacing constraints or obstacles. To this end, we propose a two stage process that returns locally optimal obstacle displacements to enable a feasible path for the robot. The first stage proceeds by computing a trajectory through the obstacles while minimizing an appropriate objective function. In the second stage, these obstacles are displaced to make the computed robot trajectory feasible, that is, collision-free. Several examples are provided that successfully demonstrate our approach on two distinct classes of constraint displacement problems.

Abstract

Deep thermalization refers to the emergence of Haar-like randomness from quantum systems upon partial measurements. As a generalization of quantum thermalization, it is often associated with high complexity and entanglement. Here, we introduce computational deep thermalization and construct the fastest possible dynamics exhibiting it at infinite effective temperature. Our circuit dynamics produce quantum states with low entanglement in polylogarithmic depth that are indistinguishable from Haar random states to any computationally bounded observer. Importantly, the observer is allowed to request many copies of the same residual state obtained from partial projective measurements on the state—this condition is beyond the standard settings of quantum pseudorandomness but natural for deep thermalization. In cryptographic terms, these states are pseudorandom and pseudoentangled, and crucially, they retain these properties under local measurements. Our results demonstrate a new form of computational thermalization in which thermal-like behavior arises from structured quantum states endowed with cryptographic properties instead of from highly unstructured ensembles. The low resource complexity of preparing these states suggests scalable simulations of deep thermalization using quantum computers. Our Letter also motivates the study of computational quantum pseudorandomness beyond BQP observers.