arXiv subjects

Shiqing Li

Publications and source records attributed to Shiqing Li.

5 recordsLinked to original sources

arXiv2026

EvoLP: Self-Evolving Latency Predictor for Model Compression in Real-Time Edge Systems

Edge devices are increasingly utilized for deploying deep learning applications on embedded systems. The real-time nature of many applications and the limited resources of edge devices necessitate latency-targeted neural network compression. However, measuring latency on real devices is challenging and expensive. Therefore, this letter presents a novel and efficient framework, named EvoLP, to accurately predict the inference latency of models on edge devices. This predictor can evolve to achieve higher latency prediction precision during the network compression process. Experimental results demonstrate that EvoLP outperforms previous state-of-the-art approaches by being evaluated on three edge devices and four model variants. Moreover, when incorporated into a model compression framework, it effectively guides the compression process for higher model accuracy while satisfying strict latency constraints. We open source EvoLP at https://github.com/ntuliuteam/EvoLP.

cs.LG↗

arXiv2026

CRIMP: Compact & Reliable DNN Inference on In-Memory Processing via Crossbar-Aligned Compression and Non-ideality Adaptation

Crossbar-based In-Memory Processing (IMP) accelerators achieve high-speed, low-power computing for deep neural networks (DNNs), but face three obstacles. First, floating-point (FP) arithmetic is incompatible with crossbars, and existing quantization schemes still require FP processors for scaling factors, incurring hardware overhead. Second, redundant DNN parameters occupy too many crossbars, and current IMP-aware pruning methods require data aligning across crossbars, introducing significant memory and computing overhead. Third, non-ideal crossbar behaviors such as write variations degrade the accuracy of deployed models, and existing compensation methods add substantial overhead. In this paper, we address all three problems within a single training process. We reuse bit-shift units in crossbars to approximately multiply scaling factors, avoiding FP processors. We apply kernel-group pruning and crossbar pruning to remove the hardware units needed for data aligning. We adopt runtime-aware non-ideality adaptation to relieve the impact of device non-ideality from the training stage by exploiting crossbar features. Integrating these three optimizations into one comprehensive learning framework reduces training overhead and improves accuracy. Experiments show that our quantization incurs a negligible accuracy drop, and our pruning achieves higher sparsity and accuracy than state-of-the-art methods. Our framework produces integer-only, pruned, and reliable VGG-16 and ResNet-56 models for CIFAR-10 on IMP accelerators, with accuracy drops of only 2.19% and 1.26%, respectively, without hardware overhead.

cs.AR↗

arXiv2026

Full-channel wavefront manipulation of surface waves with chirality-assisted geometric-phase metasurface

Owing to their localized field enhancement and subwavelength resolution, surface waves (SWs) offer broad application potential in communications, sensing, and photonics via on-chip wavefront manipulation. This makes multi-channel SW wavefront manipulation highly desirable. However, conventional metasurfaces for SW wavefront shaping, relying on geometric and propagation phase mechanisms, typically exhibit similar functionalities for co- or cross-polarized output channels under different circularly polarized (CP) incidences, thereby limiting the development of high-capacity on-chip integrated devices. Here, by introducing the chirality-assisted phase as an additional phase control mechanism, we effectively decouple both co- and cross-polarized output channels, enabling independent SW wavefront shaping in four distinct channels. We numerically and experimentally demonstrate two metasurfaces in the microwave range: a four-channel SW meta-deflector and a four-channel SW metadevice that simultaneously produces a focused SW beam, a SW Bessel beam, and two deflected SW beams in different directions. Therefore, chirality-assisted geometric-phase metasurfaces provide a versatile platform for multi-channel SW wavefront engineering, offering significant potential for high-capacity on-chip communication and integrated photonic systems.

physics.optics↗

arXiv2023

Unidirectional guided-wave-driven metasurfaces for arbitrary wavefront control

Metasurfaces, composed of subwavelength electromagnetic microstructures, known as meta-atoms, are capable of reshaping the wavefronts of incident beams in desired manners, making them great candidates for revolutionizing conventional optics. However, the requirement for external light excitation and the resonant nature of meta-atoms make it difficult to fully integrate metasurfaces on-chip or to control wavefronts at deep-subwavelength scales. Here, we introduce the concept and design of a new class of metasurfaces, driven by unidirectional guided waves, and being capable of arbitrary wavefront control based on the unique dispersion properties of unidirectional guided waves rather than resonant meta-atoms. Upon experimentally demonstrating the feasibility and practicality of the unidirectional nature of our designs in the microwave regime, we numerically validate this new principle through the design of several microwave meta-devices using metal-air-gyromagnetic unidirectional surface magnetoplamons, agilely converting unidirectional guided modes into the wavefronts of 3D Bessel beams, focused waves, and controllable vortex beams. We also numerically demonstrate sub-diffraction focusing, which is currently beyond the capability of conventional metasurfaces. Furthermore, we directly show how these concepts can be transferred to the terahertz regime, and discuss their feasibility in the optical domain, too. Based on this nonresonant (that is, broadband) mechanism and on standard plasmonic platforms, our metasurfaces can be integrated on-chip, enabling the manipulation of electromagnetic waves on deep subwavelength scales and over wide frequency ranges, thereby opening up new opportunities for applications in communications, remote sensing, displays, and so forth.

physics.optics↗

arXiv2020

Meta-Learning for Natural Language Understanding under Continual Learning Framework

Neural network has been recognized with its accomplishments on tackling various natural language understanding (NLU) tasks. Methods have been developed to train a robust model to handle multiple tasks to gain a general representation of text. In this paper, we implement the model-agnostic meta-learning (MAML) and Online aware Meta-learning (OML) meta-objective under the continual framework for NLU tasks. We validate our methods on selected SuperGLUE and GLUE benchmark.

cs.CL↗