Selected work

Representative Papers

SATA: Sparsity-Aware Scheduling for Selective Token Attention

Jan 28, 2026

This work addresses the hardware inefficiency of Transformer attention mechanisms stemming from their quadratic complexity, particularly exacerbated by sparse and irregular selective token attention patterns that incur substantial memory access overhead. To mitigate this, the authors propose a data locality–centric dynamic scheduling mechanism that, for the first time, integrates sparse access patterns with a runtime trace-driven control-compute co-design architecture. By reordering operand streams and employing prefetching and release strategies for intermediate Query/Key vectors, the approach efficiently manages irregular data flows with minimal scheduling overhead. Experimental results demonstrate that the proposed method achieves up to 1.76× higher system throughput and improves energy efficiency by up to 2.94× compared to existing solutions.

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Spectral Identifiability for Interpretable Probe Geometry

Nov 20, 2025

Linear probes are widely used for interpreting and evaluating neural representations, yet their reliability lacks theoretical grounding, often exhibiting abrupt performance transitions or outright failure. To address this, we propose the Spectral Identifiability Principle (SIP), the first theoretical framework linking probe stability to the spectral geometry of representations. SIP quantitatively relates spectral gaps in the representation covariance to the Fisher estimation error, thereby characterizing the phase-transition mechanism governing probe performance. It yields verifiable stability criteria that enable early detection of probe failure—going beyond conventional generalization bounds. Our method integrates finite-sample analysis, spectral graph theory, and the Fisher information framework. On synthetic data, we analytically compute and empirically validate SIP-predicted phase transitions. Experiments demonstrate that spectral analysis reliably identifies unstable probes, substantially enhancing the trustworthiness and interpretability of neural representation evaluation.

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Spectral Thresholds for Identifiability and Stability:Finite-Sample Phase Transitions in High-Dimensional Learning

Oct 04, 2025

In high-dimensional learning, model stability undergoes a sharp phase transition when the sample size $n$ falls below a critical threshold—caused by the weakest Fisher direction being overwhelmed by sampling noise, rendering parameters unidentifiable. To address this, we develop the first non-asymptotic, necessary, and verifiable stability criterion, grounded in the minimal Fisher eigenvalue, and establish a finite-sample phase transition theory that precisely characterizes the phase boundary at the $d/n$ scale. We further propose Fisher-floor regularization—a novel, smoothness- and preprocessing-invariant spectral robustness diagnostic. Integrating Fisher information spectrum analysis, finite-sample random matrix theory, and high-dimensional statistical inference, we empirically validate our framework on Gaussian mixture and logistic regression models: the predicted phase-transition threshold cleanly separates reliable estimation from instability collapse, markedly enhancing interpretability and reliability in high-dimensional modeling.

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Hardware Acceleration of Kolmogorov-Arnold Network (KAN) in Large-Scale Systems

Sep 07, 2025

To address the substantial hardware overhead and poor scalability of Kolmogorov–Arnold Networks (KANs) caused by computationally intensive B-spline evaluations, this work proposes an algorithm–hardware co-optimization architecture. First, we introduce a novel joint quantization scheme—Alignment-Symmetry and PowerGap—integrated with sparsity-aware mapping. Second, we design an N:1 time-domain modulated dynamic voltage generator to relax input precision requirements. Third, we implement an analog compute-in-memory (ACIM) circuit based on resistive random-access memory (RRAM) in 22 nm CMOS technology. Experimental results demonstrate that when scaling model parameters from 500K× to 807K×, the proposed architecture incurs only a 53% area increase, an 85% power rise, and a marginal accuracy degradation of 0.11%–0.23%. This significantly enhances scalability and energy efficiency, marking the first high-efficiency hardware acceleration of large-scale KANs.

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ID-Card Synthetic Generation: Toward a Simulated Bona fide Dataset

Aug 18, 2025

To address the severe scarcity of bona fide samples in Presentation Attack Detection (PAD), which critically undermines model robustness, this paper proposes— for the first time—the use of Stable Diffusion to generate high-fidelity synthetic ID card images, systematically augmenting bona fide training data. Unlike prior works focusing on attack sample generation, our method leverages controllable text guidance and structural priors of identity documents to synthesize diverse images exhibiting consistent texture, illumination, and geometric fidelity. The generated samples are reliably classified as bona fide by mainstream PAD models. Under limited real-data regimes, our approach significantly improves detection performance—reducing Equal Error Rate (EER) by an average of 32.7%—and generalizes effectively to unseen attack types. Experiments demonstrate that this diffusion-based data augmentation paradigm effectively alleviates the bona fide data bottleneck, offering a novel solution for low-resource PAD tasks.

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Recent publications

Latest Papers

SATA: Sparsity-Aware Scheduling for Selective Token Attention

Jan 28, 2026

This work addresses the hardware inefficiency of Transformer attention mechanisms stemming from their quadratic complexity, particularly exacerbated by sparse and irregular selective token attention patterns that incur substantial memory access overhead. To mitigate this, the authors propose a data locality–centric dynamic scheduling mechanism that, for the first time, integrates sparse access patterns with a runtime trace-driven control-compute co-design architecture. By reordering operand streams and employing prefetching and release strategies for intermediate Query/Key vectors, the approach efficiently manages irregular data flows with minimal scheduling overhead. Experimental results demonstrate that the proposed method achieves up to 1.76× higher system throughput and improves energy efficiency by up to 2.94× compared to existing solutions.

0 citationsRead paper

Spectral Identifiability for Interpretable Probe Geometry

Nov 20, 2025

Linear probes are widely used for interpreting and evaluating neural representations, yet their reliability lacks theoretical grounding, often exhibiting abrupt performance transitions or outright failure. To address this, we propose the Spectral Identifiability Principle (SIP), the first theoretical framework linking probe stability to the spectral geometry of representations. SIP quantitatively relates spectral gaps in the representation covariance to the Fisher estimation error, thereby characterizing the phase-transition mechanism governing probe performance. It yields verifiable stability criteria that enable early detection of probe failure—going beyond conventional generalization bounds. Our method integrates finite-sample analysis, spectral graph theory, and the Fisher information framework. On synthetic data, we analytically compute and empirically validate SIP-predicted phase transitions. Experiments demonstrate that spectral analysis reliably identifies unstable probes, substantially enhancing the trustworthiness and interpretability of neural representation evaluation.

0 citationsRead paper

Spectral Thresholds for Identifiability and Stability:Finite-Sample Phase Transitions in High-Dimensional Learning

Oct 04, 2025

In high-dimensional learning, model stability undergoes a sharp phase transition when the sample size $n$ falls below a critical threshold—caused by the weakest Fisher direction being overwhelmed by sampling noise, rendering parameters unidentifiable. To address this, we develop the first non-asymptotic, necessary, and verifiable stability criterion, grounded in the minimal Fisher eigenvalue, and establish a finite-sample phase transition theory that precisely characterizes the phase boundary at the $d/n$ scale. We further propose Fisher-floor regularization—a novel, smoothness- and preprocessing-invariant spectral robustness diagnostic. Integrating Fisher information spectrum analysis, finite-sample random matrix theory, and high-dimensional statistical inference, we empirically validate our framework on Gaussian mixture and logistic regression models: the predicted phase-transition threshold cleanly separates reliable estimation from instability collapse, markedly enhancing interpretability and reliability in high-dimensional modeling.

0 citationsRead paper

Hardware Acceleration of Kolmogorov-Arnold Network (KAN) in Large-Scale Systems

Sep 07, 2025

To address the substantial hardware overhead and poor scalability of Kolmogorov–Arnold Networks (KANs) caused by computationally intensive B-spline evaluations, this work proposes an algorithm–hardware co-optimization architecture. First, we introduce a novel joint quantization scheme—Alignment-Symmetry and PowerGap—integrated with sparsity-aware mapping. Second, we design an N:1 time-domain modulated dynamic voltage generator to relax input precision requirements. Third, we implement an analog compute-in-memory (ACIM) circuit based on resistive random-access memory (RRAM) in 22 nm CMOS technology. Experimental results demonstrate that when scaling model parameters from 500K× to 807K×, the proposed architecture incurs only a 53% area increase, an 85% power rise, and a marginal accuracy degradation of 0.11%–0.23%. This significantly enhances scalability and energy efficiency, marking the first high-efficiency hardware acceleration of large-scale KANs.

0 citationsRead paper

ID-Card Synthetic Generation: Toward a Simulated Bona fide Dataset

Aug 18, 2025

To address the severe scarcity of bona fide samples in Presentation Attack Detection (PAD), which critically undermines model robustness, this paper proposes— for the first time—the use of Stable Diffusion to generate high-fidelity synthetic ID card images, systematically augmenting bona fide training data. Unlike prior works focusing on attack sample generation, our method leverages controllable text guidance and structural priors of identity documents to synthesize diverse images exhibiting consistent texture, illumination, and geometric fidelity. The generated samples are reliably classified as bona fide by mainstream PAD models. Under limited real-data regimes, our approach significantly improves detection performance—reducing Equal Error Rate (EER) by an average of 32.7%—and generalizes effectively to unseen attack types. Experiments demonstrate that this diffusion-based data augmentation paradigm effectively alleviates the bona fide data bottleneck, offering a novel solution for low-resource PAD tasks.

0 citationsRead paper