Vision Transformers(ViTs)have achieved remarkable success across various artificial intelligence-based computer vision applications.However,their demanding computational and memory requirements pose significant challe...Vision Transformers(ViTs)have achieved remarkable success across various artificial intelligence-based computer vision applications.However,their demanding computational and memory requirements pose significant challenges for de-ployment on resource-constrained edge devices.Although post-training quantization(PTQ)provides a promising solution by reducing model precision with minimal calibration data,aggressive low-bit quantization typically leads to substantial perfor-mance degradation.To address this challenge,we present the truncated uniform-log2 quantizer and progressive bit-decline reconstruction method for vision Transformer quantization(TP-ViT).It is an innovative PTQ framework specifically designed for ViTs,featuring two key technical contributions:(1)truncated uniform-log2 quantizer,a novel quantization approach which effectively handles outlier values in post-Softmax activations,significantly reducing quantization errors;(2)bit-decline optimiza-tion strategy,which employs transition weights to gradually reduce bit precision while maintaining model performance under extreme quantization conditions.Comprehensive experiments on image classification,object detection,and instance segmenta-tion tasks demonstrate TP-ViT’s superior performance compared to state-of-the-art PTQ methods,particularly in challenging 3-bit quantization scenarios.Our framework achieves a notable 6.18 percentage points improvement in top-1 accuracy for ViT-small under 3-bit quantization.These results validate TP-ViT’s robustness and general applicability,paving the way for more efficient deployment of ViT models in computer vision applications on edge hardware.展开更多
This article presents a high speed third-order continuous-time(CT)sigma-delta analog-to-digital converter(SDADC)based on voltagecontrolled oscillator(VCO),featuring a digital programmable quantizer structure.To improv...This article presents a high speed third-order continuous-time(CT)sigma-delta analog-to-digital converter(SDADC)based on voltagecontrolled oscillator(VCO),featuring a digital programmable quantizer structure.To improve the overall performance,not only oversampling technique but also noise-shaping enhancing technique is used to suppress in-band noise.Due to the intrinsic first-order noise-shaping of the VCO quantizer,the proposed third-order SDADC can realize forth-order noise-shaping ideally.As a bright advantage,the proposed programmable VCO quantizer is digital-friendly,which can simplify the design process and improve antiinterference capability of the circuit.A 4-bit programmable VCO quantizer clocked at 2.5 GHz,which is proposed in a 40 nm complementary metaloxide semiconductor(CMOS)technology,consists of an analog VCO circuit and a digital programmable quantizer,achieving 50.7 dB signal-to-noise ratio(SNR)and 26.9 dB signal-to-noise-and-distortion ration(SNDR)for a 19 MHz−3.5 dBFS input signal in 78 MHz bandwidth(BW).The digital quantizer,which is programmed in the Verilog hardware description language(HDL),consists of two-stage D-flip-flop(DFF)based registers,XOR gates and an adder.The presented SDADC adopts the cascade of integrators with feed-forward summation(CIFF)structure with a third-order loop filter,operating at 2.5 GHz and showing behavioral simulation performance of 92.9 dB SNR over 78 MHz bandwidth.展开更多
In this paper, we consider the design of interconnected H-infinity feedback control systems with quantized signals. We assume that a decentralized static output feedback has been designed for an interconnected continu...In this paper, we consider the design of interconnected H-infinity feedback control systems with quantized signals. We assume that a decentralized static output feedback has been designed for an interconnected continuous-time LTI system so that the closed-loop system is stable and a desired H-infinity disturbance attenuation level is achieved, and that the subsystems' measurement outputs are quantized before they are passed to the local controller. We propose a local-output-dependent strategy for updating the quantizers' parameters, so that the overall closed-loop system is asymptotically stable and achieves the same H-infinity disturbance attenuation level. Both the pre-designed controllers and the quantizers' parameters are constructed in a decentralized manner, depending on local information.展开更多
A high-speed and high-resolution optical A/D quantizer is proposed.Its architecture is discussed.Bit circuits are built by using the phase modulators in parallel.Based on the different character of the half-wave volta...A high-speed and high-resolution optical A/D quantizer is proposed.Its architecture is discussed.Bit circuits are built by using the phase modulators in parallel.Based on the different character of the half-wave voltage for every phase modulator and the polarized bias design of incident light,the RF input signal is coled and transmitted in the form of optical digital signal.According to the principle of the architecture,the high-resolution quantizers with 8-bit and 12-bit,et al.are built,which operate at 100 GS/s.Their quantization noise is invariable almost with bit circuits increasing.The simulation result of 4-bit A/D quantizer is also given.展开更多
In this paper, the optimization of quantizer’s segment threshold is done. The quantizer is designed on the basis of approximative spline functions. Coefficients on which we form approximative spline functions are cal...In this paper, the optimization of quantizer’s segment threshold is done. The quantizer is designed on the basis of approximative spline functions. Coefficients on which we form approximative spline functions are calculated by minimization mean square error (MSE). For coefficients determined in this way, spline functions by which optimal compressor function is approximated are obtained. For the quantizer designed on the basis of approximative spline functions, segment threshold is numerically determined depending on maximal value of the signal to quantization noise ratio (SQNR). Thus, quantizer with optimized segment threshold is achieved. It is shown that by quantizer model designed in this way and proposed in this paper, the SQNR that is very close to SQNR of nonlinear optimal companding quantizer is achieved.展开更多
AVQ(Adaptive Vector Quantizer)overcomes some shortcomings of traditional vectorquantizer with a fixed codebook trained and generated by the LBG or other algorithms by applyinga variab|e codebook.In this paper,we descr...AVQ(Adaptive Vector Quantizer)overcomes some shortcomings of traditional vectorquantizer with a fixed codebook trained and generated by the LBG or other algorithms by applyinga variab|e codebook.In this paper,we describe an effective and efficient implementation of AVQby modifying the CCN(Carpenter/Grossberg Net).The encoding process of AVQ is very similarto the learning process of the CGN.We study several different encoding schemes,includingwaveform AVQ,analysed parameter AVQ and so on,implemented by the CGN.And we simulatethe encoding performance of each scheme for encoding Gaussian process source,first order Gauss-Markov process source and practical speech signal.Our simulation results show that good qualityboth in subjective and objective tests can be obtained in a low or middle bit rate range.展开更多
A new scheme is presented to design a rotated Barnes-Wall lattice based vector quantizer(LVQ). The construction method of the LVQ and its fast quantizing algorithm are described at first. Then gain-shape lattice vecto...A new scheme is presented to design a rotated Barnes-Wall lattice based vector quantizer(LVQ). The construction method of the LVQ and its fast quantizing algorithm are described at first. Then gain-shape lattice vector quantizer(GSLVQ) with LVQ as shape quantizer is discussed. Finally the GSLVQ is used in image-sequence coding and good experimental results are obtained.展开更多
We propose and demonstrate a performance-enhanced optical quantizer by inverse design.An adjoint shape cooptimization method is used to optimize the boundaries of the optical quantizer,aiming to reduce the insertion l...We propose and demonstrate a performance-enhanced optical quantizer by inverse design.An adjoint shape cooptimization method is used to optimize the boundaries of the optical quantizer,aiming to reduce the insertion loss(IL),improve the uniformity,and increase the bandwidth of the effective number of bits(ENOB).Meanwhile,the optimized shape maintains its deep ultraviolet(DUV)photolithography fabrication capability.We fabricate the device on a commercial silicon-on-insulator(SOI)platform.Measurement results show that the IL is reduced from 0.85 to 0.35 d B,and the uniformity is optimized from 1.21 to 0.24 d B at 1550 nm.The maximum ENOB increases to 3.31 bit,which is very close to the ideal value of 3.32 bit,and the bandwidth of the ENOB>3 bit is expanded to more than 50 nm.展开更多
This paper investigates the platoon control of heterogeneous vehicular cyber-physical systems(VCPSs) subject to external disturbances by using neural network and uniformly quantized communication data.To reduce the ad...This paper investigates the platoon control of heterogeneous vehicular cyber-physical systems(VCPSs) subject to external disturbances by using neural network and uniformly quantized communication data.To reduce the adverse effects of quantization errors on system performance,a coupling sliding mode surface is established for each following vehicle.The radial basis function(RBF) neural networks are employed to approximate the unknown external disturbances.Then,a novel platoon control law is proposed for cooperative tracking in which each following vehicle only uses the uniformly quantized data of the neighboring vehicles.And the designed controllers in this paper are fully distributed due to the fact that the selection of each vehicle's controller parameters is independent of the entire communication topology.The string stability of VCPSs in the entire control process is ensured rather than only ensuring the string stability after the sliding mode surface converges to zero.Compared with the existing controller design methods and quantization mechanisms,the neural adaptive sliding-mode platoon controller proposed in this paper is superior in performances including tracking errors,driving comfort and fuel economy.Numerical simulations illustrate the effectiveness and superiority of the designed control strategy.展开更多
Building on the existing symmetric quantization model of the dynamic Cournot duopoly game(CDG)with asymmetric information,we extend it to an asymmetric quantization model and study the stability of the quantum Bayesia...Building on the existing symmetric quantization model of the dynamic Cournot duopoly game(CDG)with asymmetric information,we extend it to an asymmetric quantization model and study the stability of the quantum Bayesian Nash equilibrium(QBNE)under heterogeneous expectations.We analyze the influence of various parameters on the stability of QBNE,with a particular focus on the impact of the parameter α on system stability.The results show that when α1,the quantum strategy of the symmetric quantization model is more conducive to stabilizing the market.展开更多
Quantization has emerged as an important technique for enabling efficient deployment of large language models(LLMs)by reducing their memory and computational requirements.This research conducts an evaluation of INT8 q...Quantization has emerged as an important technique for enabling efficient deployment of large language models(LLMs)by reducing their memory and computational requirements.This research conducts an evaluation of INT8 quantization on several state-of-the-art LLMs,GPT-2,LLaMA-2-7B-Chat and Qwen1.5-1.8B-Chat,across two hardware configurations:NVIDIA RTX4070 Laptop GPU and RTX4080 Laptop GPU and two tasks:text and code generation.By comparing quantized INT8 models with their FP16 counterparts and a human-written reference,we quantify the trade-offs between performance and efficiency using standard natural language generation metrics(BLEU,ROUGE-1,ROUGE-L)and semantic analysis via GPT-4o and Gemini 2.5 Flash(Google).The results reveal that INT8 post-training quantization(PTQ),hereafter referred to as INT8,substantially reduces inference time and memory footprint,with minimal impact on topical relevance but a notable decline in lexical precision,fluency and structural coherence.The extent of quality degradation varies by model size and architecture,with smaller models demonstrating greater resilience to quantization.Furthermore,we identify several limitations in quantized outputs,including reduced expressiveness,while highlighting their suitability for resource-constrained or real-time applications,such as robots monitoring safety standards in manufacturing environments.On average,INT8 quantization results in a 3.4 times speedup over FP16 inference across all tested models and GPUs(excluding configurations affected by CPU offloading),with the largest gains observed in large models like LLaMA-2-7B-Chat.The results also indicate that structured code generation exhibits slightly greater sensitivity to INT8 quantization compared to explanatory text generation.展开更多
We present the geodesic nature and quantization of geometric shift vector in quantum systems,with the parameter space defined by the Bloch momentum,using the Wilson loop approach.Our analysis extends to include bosoni...We present the geodesic nature and quantization of geometric shift vector in quantum systems,with the parameter space defined by the Bloch momentum,using the Wilson loop approach.Our analysis extends to include bosonic phonon drag shift vectors with non-vertical transitions.We demonstrate that the gauge invariant shift vector can be quantized as integer values,analogous to the Euler characteristic based on the Gauss-Bonnet theorem for a manifold with a smooth boundary.We reveal intricate relationships among geometric quantities such as the shift vector,Berry curvature,and quantum metric.Our findings demonstrate that the loop integral of the shift vector in the quantized interband formula contributes to the non-quantized component of the trace of conductivity in the circular photogalvanic effect.The Wilson loop method facilitates first-principles calculations,providing insights in the geometric underpinnings of these interband gauge invariant quantities and shedding light on their nonlinear optical manifestations in real materials.展开更多
We perform the manifestly covariant quantization of f(R)gravity in the de Donder gauge condition(or harmonic gauge condition)for general coordinate invariance.We explicitly calculate various equal-time commutation rel...We perform the manifestly covariant quantization of f(R)gravity in the de Donder gauge condition(or harmonic gauge condition)for general coordinate invariance.We explicitly calculate various equal-time commutation relations(ETCRs),in particular the ETCR between the metric and its time derivative,and show that it has a nonvanishing and nontrivial expression,whose situation should be contrasted to the previous result in higher-derivative or quadratic gravity where the ETCR was found to be identically vanishing.We also clarify global symmetries,the physical content of f(R)gravity,and clearly show that this theory is manifestly unitary and has a massive scalar and massless graviton as physical modes.展开更多
This paper introduces MobiIris,a lightweight deep network for mobile iris recognition that enhances attention and specifically addresses the balance between accuracy and efficiency on devices with limited resources.Th...This paper introduces MobiIris,a lightweight deep network for mobile iris recognition that enhances attention and specifically addresses the balance between accuracy and efficiency on devices with limited resources.The proposed model is based on the large version of MobileNetV3 and adds more spatial attention blocks and an embedding-based head that was trained using margin-based triplet learning,enabling fine-grained modeling of iris textures in a compact representation.To further improve discriminability,we design a training pipeline that combines dynamic-margin triplet loss,a staged hard/semi-hard negative mining strategy,and feature-level knowledge distillation from a ResNet-50 teacher.Finally,we investigate the use of post-training float16 quantization to reduce memory footprint and latency for deployment on mobile hardware.Experiments on the challenging CASIA-IrisV4-Thousand dataset show that the full-precision MobiIris model requires only 12 MB of storage and 27 ms inference latency,while achieving an EER of 1.409%,VR@FAR=1%of 98.184%,and CMC@1 of 94.785%,closely matching a ResNet-50 baseline that is more than 7×larger and slower.Under post-training quantization,the model shrinks to 5.94 MB with 13 ms latency and maintains a competitive balance between accuracy and efficiency compared to other optimized variants.These results demonstrate that a coherent combination of lightweight architecture design,attention mechanisms,metric-learning objectives,hard negative mining,and knowledge distillation yields a practical iris recognition solution suitable for secure,real-time authentication on mobile and embedded platforms.展开更多
The proliferation of Internet of Things(IoT)devices has introduced unprecedented security challenges,necessitating efficient intrusion detection systems(IDS)capable of operating under severe resource constraints.This ...The proliferation of Internet of Things(IoT)devices has introduced unprecedented security challenges,necessitating efficient intrusion detection systems(IDS)capable of operating under severe resource constraints.This research presents a hardware-informed empirical study of quantized neural-network-based intrusion detection for resource-constrained IoT platforms,using an ARM Cortex-M4 deployment target as a reference.We evaluate FP32,FP16,and INT8 TensorFlow Lite model variants derived from a lightweight 1D-CNN and assess their trade-offs in clean-data accuracy,model size,estimated inference latency,estimated energy consumption,and adversarial robustness.INT8-quantized model achieves 99.10%accuracy on clean data while maintaining 97.50%adversarial accuracy under Projected Gradient Descent(PGD)attacks with perturbation budget=0.3.The quantized model achieves 12.0×latency reduction(0.083 vs.0.995 ms)and 92.7%energy reduction(0.0083 vs.0.1135 mJ)when compared to FP32.The memory footprint of the model is reduced by 55.7%from 58.02 to 25.72 KB.Our comprehensive analysis includes confusion matrices,ROC curves(AUC=0.9964 for INT8),adversarial robustness heatmaps,and statistical significance testing via McNemar’s test.The results establish INT8 quantization as a viable solution for deploying robust IDS on resource-constrained IoT devices,achieving practical deployment feasibility without reducing detection performance.展开更多
We introduce the DARE-Q(Distribution-Aware Residual Entropy Quantization)method—a post-training quantization method for neural network weights designed to reduce bit-width with minimal degradation of model quality.Un...We introduce the DARE-Q(Distribution-Aware Residual Entropy Quantization)method—a post-training quantization method for neural network weights designed to reduce bit-width with minimal degradation of model quality.Unlike traditional approaches that solely optimize the mean squared error of weight approximation,DARE-Q additionally considers the entropy of the quantization residual,allowing for control over the statistical properties of the resulting error.The method is based on channel-wise symmetric uniform quantization with scaling based on a combined loss function that includes L2 distortion and entropy regularization.The DARE-Q method is implemented as a compact DAREQuantLinear module which can be easily integrated into standard transformer pipelines without changing the inference logic or using specific kernels.The experimental analysis was conducted on the language models facebook/opt-125m and facebook/opt-350m,which contain approximately 125 and 350 million parameters.The quality of the models was assessed using the standard perplexity metric(PPL)computed on the wikitext-2-raw-v1 dataset.DARE-Q is completely data-free and does not require model retraining or calibration data,which makes it the only viable option in privacy-sensitive or confidential environments where access to the original training data is restricted—precisely the setting where methods such as GPTQ and AWQ cannot be applied.The observed increase in PPL relative to data-dependent baselines reflects this fundamental trade-off rather than a shortcoming of the approach.By leveraging per-channel scale selection and a combined loss function,DARE-Q provides a flexible trade-off between approximation accuracy and quantization error structure,creating an attractive algorithmic basis for further improvement of model compression methods.展开更多
We aim to clarify the confusion and inconsistency in our recent works(Luo et al 2023 Commun.Theor.Phys.75095702;Liang et al 2024 Phys.Rev.B 110075125),and to address the incompleteness therein.In order to avoid the il...We aim to clarify the confusion and inconsistency in our recent works(Luo et al 2023 Commun.Theor.Phys.75095702;Liang et al 2024 Phys.Rev.B 110075125),and to address the incompleteness therein.In order to avoid the ill-defined nature of the free propagator of the gauge field in the ordered states of the t-J model,we adopted a gauge fixing that was not of the Becchi-Rouet-Stora-Tyutin(BRST)exact form in our previous work(Liang et al 2024 Phys.Rev.B 110075125).This led to the situation where Dirac's second-class constraints,namely,the slave particle number constraint and the Ioffe-Larkin current constraint,were not rigorously obeyed.Here we show that a consistent gauge fixing condition that enforces the exact constraints is BRST-exact in our theory.An example is the Lorenz gauge.On the other hand,we prove that although the free propagator of the gauge field in the Lorenz gauge is ill-defined,the full propagator is still well-defined.This implies that the strongly correlated t-J model can be exactly mapped to a perturbatively controllable theory within the slave particle representation.展开更多
Background:Long multi-channel ECG recordings that capture both time and space fluctuations are necessary for the accurate identification of atrial fibrillation(AF)in wearable or edge devices.These devices must use low...Background:Long multi-channel ECG recordings that capture both time and space fluctuations are necessary for the accurate identification of atrial fibrillation(AF)in wearable or edge devices.These devices must use low-power computing and effective,real-time compression to manage massive volumes of data.An energy-efficient AdderNet-based method for AF detection utilizing compressed multi-channel ECG signals was presented in this paper.Methods:A Lossless Multi-channel Adaptive Compression Engine(L-MACE)with training,spatial,and temporal compression units is proposed in order to control bandwidth and storage constraints.Instead of employing sophisticated predictors,it uses a basic Sum-Predictor(SP)and a minimum spanning tree to decrease inter-channel redundancy.Compression is made even easier with a Booth-Encoded Logarithmic Quantized(B-LQ)multiplier.In order to save resources,a Quantized AdderNet(Q-ANet)uses sum-of-absolute-differences(SAD)rather than multiply-accumulate to detect AF after compression.Memory use is decreased by an activation-guided quantization technique,while hardware and energy efficiency is increased with SP adders.Results:The proposed method offers an accuracy of 99.07%,a precision of 99.45%,a sensitivity of 99.33%,a specificity of 98.22%,and an F1 score of 99.39%.The proposed B-LQ multiplier achieves 316 LUT and 1.758 ns of latency,while the proposed SP adder achieves 40 LUT,74 IO,and 1.610 ns of delay.Conclusion:Overall,by combining hardware-optimized computation and compression-aware signal processing,the proposed architecture shows an incredibly efficient solution for real-time AF identification in edge and wearable devices.展开更多
The paper is devoted to noncommutative formal geometry of a contractive quantum plane,whose spectrum is the union of two copies of the complex plane.It turns out that a formal completion of the Arens-Michael envelope ...The paper is devoted to noncommutative formal geometry of a contractive quantum plane,whose spectrum is the union of two copies of the complex plane.It turns out that a formal completion of the Arens-Michael envelope of a contractive quantum plane results in a noncommutative analytic space,whose base topological space is the same spectrum,whereas the structure sheaf is obtained as a certain quantization of the related commutative analytic space.As the basic tool we use the fibered products of the Fréchet sheaves.The related topological homology problems are considered to find out a key link between the transversality relation of the noncommutative sections versus to a left Fréchet module,and noncommutative Taylor spectrum of the module.展开更多
In this paper,a distributed Event-Triggered(ET)collision avoidance coordinated control for Quadrotor Unmanned Aerial Vehicles(QUAVs)is proposed based on Virtual Tubes(VTs)with flexible boundaries in the presence of un...In this paper,a distributed Event-Triggered(ET)collision avoidance coordinated control for Quadrotor Unmanned Aerial Vehicles(QUAVs)is proposed based on Virtual Tubes(VTs)with flexible boundaries in the presence of unknown external disturbances.Firstly,VTs are constructed for each QUAV,and the QUAV is restricted into the corresponding VT by the artificial potential field,which is distributed around the boundary of the VT.Thus,the collisions between QUAVs are avoided.Besides,the boundaries of the VTs are flexible by the modification signals,which are generated by the self-regulating auxiliary systems,to make the repulsive force smaller and give more buffer space for QUAVs without collision.Then,a novel ET mechanism is designed by introducing the concept of prediction to the traditional fixed threshold ET mechanism.Furthermore,a disturbance observer is proposed to deal with the adverse effects of the unknown external disturbance.On this basis,a distributed ET collision avoidance coordinated controller is proposed.Then,the proposed controller is quantized by the hysteresis uniform quantizer and then sent to the actuator only at the ET instants.The boundedness of the closed-loop signals is verified by the Lyapunov method.Finally,simulation and experimental results are performed to demonstrate the superiority of the proposed control method.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62301092 and 62301093).
摘要Vision Transformers(ViTs)have achieved remarkable success across various artificial intelligence-based computer vision applications.However,their demanding computational and memory requirements pose significant challenges for de-ployment on resource-constrained edge devices.Although post-training quantization(PTQ)provides a promising solution by reducing model precision with minimal calibration data,aggressive low-bit quantization typically leads to substantial perfor-mance degradation.To address this challenge,we present the truncated uniform-log2 quantizer and progressive bit-decline reconstruction method for vision Transformer quantization(TP-ViT).It is an innovative PTQ framework specifically designed for ViTs,featuring two key technical contributions:(1)truncated uniform-log2 quantizer,a novel quantization approach which effectively handles outlier values in post-Softmax activations,significantly reducing quantization errors;(2)bit-decline optimiza-tion strategy,which employs transition weights to gradually reduce bit precision while maintaining model performance under extreme quantization conditions.Comprehensive experiments on image classification,object detection,and instance segmenta-tion tasks demonstrate TP-ViT’s superior performance compared to state-of-the-art PTQ methods,particularly in challenging 3-bit quantization scenarios.Our framework achieves a notable 6.18 percentage points improvement in top-1 accuracy for ViT-small under 3-bit quantization.These results validate TP-ViT’s robustness and general applicability,paving the way for more efficient deployment of ViT models in computer vision applications on edge hardware.
基金This work was supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No.18KJB510045.
摘要This article presents a high speed third-order continuous-time(CT)sigma-delta analog-to-digital converter(SDADC)based on voltagecontrolled oscillator(VCO),featuring a digital programmable quantizer structure.To improve the overall performance,not only oversampling technique but also noise-shaping enhancing technique is used to suppress in-band noise.Due to the intrinsic first-order noise-shaping of the VCO quantizer,the proposed third-order SDADC can realize forth-order noise-shaping ideally.As a bright advantage,the proposed programmable VCO quantizer is digital-friendly,which can simplify the design process and improve antiinterference capability of the circuit.A 4-bit programmable VCO quantizer clocked at 2.5 GHz,which is proposed in a 40 nm complementary metaloxide semiconductor(CMOS)technology,consists of an analog VCO circuit and a digital programmable quantizer,achieving 50.7 dB signal-to-noise ratio(SNR)and 26.9 dB signal-to-noise-and-distortion ration(SNDR)for a 19 MHz−3.5 dBFS input signal in 78 MHz bandwidth(BW).The digital quantizer,which is programmed in the Verilog hardware description language(HDL),consists of two-stage D-flip-flop(DFF)based registers,XOR gates and an adder.The presented SDADC adopts the cascade of integrators with feed-forward summation(CIFF)structure with a third-order loop filter,operating at 2.5 GHz and showing behavioral simulation performance of 92.9 dB SNR over 78 MHz bandwidth.
基金supported by the Japan Ministry of Education,Sciences and Culture under Grant-in-Aid for Scientific Research(C)(No.21560471)
摘要In this paper, we consider the design of interconnected H-infinity feedback control systems with quantized signals. We assume that a decentralized static output feedback has been designed for an interconnected continuous-time LTI system so that the closed-loop system is stable and a desired H-infinity disturbance attenuation level is achieved, and that the subsystems' measurement outputs are quantized before they are passed to the local controller. We propose a local-output-dependent strategy for updating the quantizers' parameters, so that the overall closed-loop system is asymptotically stable and achieves the same H-infinity disturbance attenuation level. Both the pre-designed controllers and the quantizers' parameters are constructed in a decentralized manner, depending on local information.
基金Natural Science Foundation from Colleges and Universities of Jiangsu Province(04KJD140033)
摘要A high-speed and high-resolution optical A/D quantizer is proposed.Its architecture is discussed.Bit circuits are built by using the phase modulators in parallel.Based on the different character of the half-wave voltage for every phase modulator and the polarized bias design of incident light,the RF input signal is coled and transmitted in the form of optical digital signal.According to the principle of the architecture,the high-resolution quantizers with 8-bit and 12-bit,et al.are built,which operate at 100 GS/s.Their quantization noise is invariable almost with bit circuits increasing.The simulation result of 4-bit A/D quantizer is also given.
基金Serbian Ministry of Education and Science through Mathematical Institute of Serbian Academy of Sciences and Arts(Project III44006)Serbian Ministry of Education and Science(Project TR32035)
摘要In this paper, the optimization of quantizer’s segment threshold is done. The quantizer is designed on the basis of approximative spline functions. Coefficients on which we form approximative spline functions are calculated by minimization mean square error (MSE). For coefficients determined in this way, spline functions by which optimal compressor function is approximated are obtained. For the quantizer designed on the basis of approximative spline functions, segment threshold is numerically determined depending on maximal value of the signal to quantization noise ratio (SQNR). Thus, quantizer with optimized segment threshold is achieved. It is shown that by quantizer model designed in this way and proposed in this paper, the SQNR that is very close to SQNR of nonlinear optimal companding quantizer is achieved.
摘要AVQ(Adaptive Vector Quantizer)overcomes some shortcomings of traditional vectorquantizer with a fixed codebook trained and generated by the LBG or other algorithms by applyinga variab|e codebook.In this paper,we describe an effective and efficient implementation of AVQby modifying the CCN(Carpenter/Grossberg Net).The encoding process of AVQ is very similarto the learning process of the CGN.We study several different encoding schemes,includingwaveform AVQ,analysed parameter AVQ and so on,implemented by the CGN.And we simulatethe encoding performance of each scheme for encoding Gaussian process source,first order Gauss-Markov process source and practical speech signal.Our simulation results show that good qualityboth in subjective and objective tests can be obtained in a low or middle bit rate range.
基金Supported in part by subject 863-317 (China Communication 863 Programme)Fund of Xidian University and ISN National Key Lab
摘要A new scheme is presented to design a rotated Barnes-Wall lattice based vector quantizer(LVQ). The construction method of the LVQ and its fast quantizing algorithm are described at first. Then gain-shape lattice vector quantizer(GSLVQ) with LVQ as shape quantizer is discussed. Finally the GSLVQ is used in image-sequence coding and good experimental results are obtained.
基金supported by the National Natural Science Foundation of China(Nos.61935003 and 62275029)。
摘要We propose and demonstrate a performance-enhanced optical quantizer by inverse design.An adjoint shape cooptimization method is used to optimize the boundaries of the optical quantizer,aiming to reduce the insertion loss(IL),improve the uniformity,and increase the bandwidth of the effective number of bits(ENOB).Meanwhile,the optimized shape maintains its deep ultraviolet(DUV)photolithography fabrication capability.We fabricate the device on a commercial silicon-on-insulator(SOI)platform.Measurement results show that the IL is reduced from 0.85 to 0.35 d B,and the uniformity is optimized from 1.21 to 0.24 d B at 1550 nm.The maximum ENOB increases to 3.31 bit,which is very close to the ideal value of 3.32 bit,and the bandwidth of the ENOB>3 bit is expanded to more than 50 nm.
基金supported by the National Natural Science Foundation of China(62173079,62473203)Liaoning Provincial Science and Technology Plan Joint Program(2024-MSLH-019)+1 种基金the Education Department of Liaoning Province(LJKMZ20221840)Interdisciplinary project of Dalian University(DLUXK-2024-YB-004)。
摘要This paper investigates the platoon control of heterogeneous vehicular cyber-physical systems(VCPSs) subject to external disturbances by using neural network and uniformly quantized communication data.To reduce the adverse effects of quantization errors on system performance,a coupling sliding mode surface is established for each following vehicle.The radial basis function(RBF) neural networks are employed to approximate the unknown external disturbances.Then,a novel platoon control law is proposed for cooperative tracking in which each following vehicle only uses the uniformly quantized data of the neighboring vehicles.And the designed controllers in this paper are fully distributed due to the fact that the selection of each vehicle's controller parameters is independent of the entire communication topology.The string stability of VCPSs in the entire control process is ensured rather than only ensuring the string stability after the sliding mode surface converges to zero.Compared with the existing controller design methods and quantization mechanisms,the neural adaptive sliding-mode platoon controller proposed in this paper is superior in performances including tracking errors,driving comfort and fuel economy.Numerical simulations illustrate the effectiveness and superiority of the designed control strategy.
基金Project supported by the National Natural Science Foundation of China(Grant No.12461054)the Science and Technology Key Foundation of Guizhou Province,China(Grant No.2025089)。
摘要Building on the existing symmetric quantization model of the dynamic Cournot duopoly game(CDG)with asymmetric information,we extend it to an asymmetric quantization model and study the stability of the quantum Bayesian Nash equilibrium(QBNE)under heterogeneous expectations.We analyze the influence of various parameters on the stability of QBNE,with a particular focus on the impact of the parameter α on system stability.The results show that when α1,the quantum strategy of the symmetric quantization model is more conducive to stabilizing the market.
基金supported by a grant of the Ministry of Research,Innovation and Digitization,CNCS/CCCDI-UEFISCDI,project number COFUND-DUT-OPEN4CEC-1,within PNCDI Ⅳfunded by UEFISCDI under the Driving Urban Transitions Partnership,which has been co-funded by the European Commission.
摘要Quantization has emerged as an important technique for enabling efficient deployment of large language models(LLMs)by reducing their memory and computational requirements.This research conducts an evaluation of INT8 quantization on several state-of-the-art LLMs,GPT-2,LLaMA-2-7B-Chat and Qwen1.5-1.8B-Chat,across two hardware configurations:NVIDIA RTX4070 Laptop GPU and RTX4080 Laptop GPU and two tasks:text and code generation.By comparing quantized INT8 models with their FP16 counterparts and a human-written reference,we quantify the trade-offs between performance and efficiency using standard natural language generation metrics(BLEU,ROUGE-1,ROUGE-L)and semantic analysis via GPT-4o and Gemini 2.5 Flash(Google).The results reveal that INT8 post-training quantization(PTQ),hereafter referred to as INT8,substantially reduces inference time and memory footprint,with minimal impact on topical relevance but a notable decline in lexical precision,fluency and structural coherence.The extent of quality degradation varies by model size and architecture,with smaller models demonstrating greater resilience to quantization.Furthermore,we identify several limitations in quantized outputs,including reduced expressiveness,while highlighting their suitability for resource-constrained or real-time applications,such as robots monitoring safety standards in manufacturing environments.On average,INT8 quantization results in a 3.4 times speedup over FP16 inference across all tested models and GPUs(excluding configurations affected by CPU offloading),with the largest gains observed in large models like LLaMA-2-7B-Chat.The results also indicate that structured code generation exhibits slightly greater sensitivity to INT8 quantization compared to explanatory text generation.
基金supported by the National Natural Science Foundation of China(Grant Nos.12522411,12304049,and 12474240 for H.W.,92265203 and 12488101 for K.C.)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant Nos.XDB28000000 and XDB0460000 for K.C.)+1 种基金the Innovation Program for Quantum Science and Technology(Grant No.2024ZD0300104 for K.C.)the Fundamental Research Funds for the Central Universities(for H.W.)。
摘要We present the geodesic nature and quantization of geometric shift vector in quantum systems,with the parameter space defined by the Bloch momentum,using the Wilson loop approach.Our analysis extends to include bosonic phonon drag shift vectors with non-vertical transitions.We demonstrate that the gauge invariant shift vector can be quantized as integer values,analogous to the Euler characteristic based on the Gauss-Bonnet theorem for a manifold with a smooth boundary.We reveal intricate relationships among geometric quantities such as the shift vector,Berry curvature,and quantum metric.Our findings demonstrate that the loop integral of the shift vector in the quantized interband formula contributes to the non-quantized component of the trace of conductivity in the circular photogalvanic effect.The Wilson loop method facilitates first-principles calculations,providing insights in the geometric underpinnings of these interband gauge invariant quantities and shedding light on their nonlinear optical manifestations in real materials.
摘要We perform the manifestly covariant quantization of f(R)gravity in the de Donder gauge condition(or harmonic gauge condition)for general coordinate invariance.We explicitly calculate various equal-time commutation relations(ETCRs),in particular the ETCR between the metric and its time derivative,and show that it has a nonvanishing and nontrivial expression,whose situation should be contrasted to the previous result in higher-derivative or quadratic gravity where the ETCR was found to be identically vanishing.We also clarify global symmetries,the physical content of f(R)gravity,and clearly show that this theory is manifestly unitary and has a massive scalar and massless graviton as physical modes.
摘要This paper introduces MobiIris,a lightweight deep network for mobile iris recognition that enhances attention and specifically addresses the balance between accuracy and efficiency on devices with limited resources.The proposed model is based on the large version of MobileNetV3 and adds more spatial attention blocks and an embedding-based head that was trained using margin-based triplet learning,enabling fine-grained modeling of iris textures in a compact representation.To further improve discriminability,we design a training pipeline that combines dynamic-margin triplet loss,a staged hard/semi-hard negative mining strategy,and feature-level knowledge distillation from a ResNet-50 teacher.Finally,we investigate the use of post-training float16 quantization to reduce memory footprint and latency for deployment on mobile hardware.Experiments on the challenging CASIA-IrisV4-Thousand dataset show that the full-precision MobiIris model requires only 12 MB of storage and 27 ms inference latency,while achieving an EER of 1.409%,VR@FAR=1%of 98.184%,and CMC@1 of 94.785%,closely matching a ResNet-50 baseline that is more than 7×larger and slower.Under post-training quantization,the model shrinks to 5.94 MB with 13 ms latency and maintains a competitive balance between accuracy and efficiency compared to other optimized variants.These results demonstrate that a coherent combination of lightweight architecture design,attention mechanisms,metric-learning objectives,hard negative mining,and knowledge distillation yields a practical iris recognition solution suitable for secure,real-time authentication on mobile and embedded platforms.
摘要The proliferation of Internet of Things(IoT)devices has introduced unprecedented security challenges,necessitating efficient intrusion detection systems(IDS)capable of operating under severe resource constraints.This research presents a hardware-informed empirical study of quantized neural-network-based intrusion detection for resource-constrained IoT platforms,using an ARM Cortex-M4 deployment target as a reference.We evaluate FP32,FP16,and INT8 TensorFlow Lite model variants derived from a lightweight 1D-CNN and assess their trade-offs in clean-data accuracy,model size,estimated inference latency,estimated energy consumption,and adversarial robustness.INT8-quantized model achieves 99.10%accuracy on clean data while maintaining 97.50%adversarial accuracy under Projected Gradient Descent(PGD)attacks with perturbation budget=0.3.The quantized model achieves 12.0×latency reduction(0.083 vs.0.995 ms)and 92.7%energy reduction(0.0083 vs.0.1135 mJ)when compared to FP32.The memory footprint of the model is reduced by 55.7%from 58.02 to 25.72 KB.Our comprehensive analysis includes confusion matrices,ROC curves(AUC=0.9964 for INT8),adversarial robustness heatmaps,and statistical significance testing via McNemar’s test.The results establish INT8 quantization as a viable solution for deploying robust IDS on resource-constrained IoT devices,achieving practical deployment feasibility without reducing detection performance.
基金supported by grant No.25-71-10012 from the Russian Science Foundation,http://gffzz5363282ec1d94f2dswububn6xf65v6pqx.ffgz.tsg.suse.edu.cn/project/25-71-10012/.
摘要We introduce the DARE-Q(Distribution-Aware Residual Entropy Quantization)method—a post-training quantization method for neural network weights designed to reduce bit-width with minimal degradation of model quality.Unlike traditional approaches that solely optimize the mean squared error of weight approximation,DARE-Q additionally considers the entropy of the quantization residual,allowing for control over the statistical properties of the resulting error.The method is based on channel-wise symmetric uniform quantization with scaling based on a combined loss function that includes L2 distortion and entropy regularization.The DARE-Q method is implemented as a compact DAREQuantLinear module which can be easily integrated into standard transformer pipelines without changing the inference logic or using specific kernels.The experimental analysis was conducted on the language models facebook/opt-125m and facebook/opt-350m,which contain approximately 125 and 350 million parameters.The quality of the models was assessed using the standard perplexity metric(PPL)computed on the wikitext-2-raw-v1 dataset.DARE-Q is completely data-free and does not require model retraining or calibration data,which makes it the only viable option in privacy-sensitive or confidential environments where access to the original training data is restricted—precisely the setting where methods such as GPTQ and AWQ cannot be applied.The observed increase in PPL relative to data-dependent baselines reflects this fundamental trade-off rather than a shortcoming of the approach.By leveraging per-channel scale selection and a combined loss function,DARE-Q provides a flexible trade-off between approximation accuracy and quantization error structure,creating an attractive algorithmic basis for further improvement of model compression methods.
基金supported by the National Natural Science Foundation of China with Grants No.12174067(X.L.and Y.Y.),No.12204329(L.L.),No.12135018(T.S.),and No.12047503(T.S.)supported by National Key Research and Development Program of China with Grant No.2021YFA0718304by CAS Project for Young Scientists in Basic Research with Grant No.YSBR-057。
摘要We aim to clarify the confusion and inconsistency in our recent works(Luo et al 2023 Commun.Theor.Phys.75095702;Liang et al 2024 Phys.Rev.B 110075125),and to address the incompleteness therein.In order to avoid the ill-defined nature of the free propagator of the gauge field in the ordered states of the t-J model,we adopted a gauge fixing that was not of the Becchi-Rouet-Stora-Tyutin(BRST)exact form in our previous work(Liang et al 2024 Phys.Rev.B 110075125).This led to the situation where Dirac's second-class constraints,namely,the slave particle number constraint and the Ioffe-Larkin current constraint,were not rigorously obeyed.Here we show that a consistent gauge fixing condition that enforces the exact constraints is BRST-exact in our theory.An example is the Lorenz gauge.On the other hand,we prove that although the free propagator of the gauge field in the Lorenz gauge is ill-defined,the full propagator is still well-defined.This implies that the strongly correlated t-J model can be exactly mapped to a perturbatively controllable theory within the slave particle representation.
摘要Background:Long multi-channel ECG recordings that capture both time and space fluctuations are necessary for the accurate identification of atrial fibrillation(AF)in wearable or edge devices.These devices must use low-power computing and effective,real-time compression to manage massive volumes of data.An energy-efficient AdderNet-based method for AF detection utilizing compressed multi-channel ECG signals was presented in this paper.Methods:A Lossless Multi-channel Adaptive Compression Engine(L-MACE)with training,spatial,and temporal compression units is proposed in order to control bandwidth and storage constraints.Instead of employing sophisticated predictors,it uses a basic Sum-Predictor(SP)and a minimum spanning tree to decrease inter-channel redundancy.Compression is made even easier with a Booth-Encoded Logarithmic Quantized(B-LQ)multiplier.In order to save resources,a Quantized AdderNet(Q-ANet)uses sum-of-absolute-differences(SAD)rather than multiply-accumulate to detect AF after compression.Memory use is decreased by an activation-guided quantization technique,while hardware and energy efficiency is increased with SP adders.Results:The proposed method offers an accuracy of 99.07%,a precision of 99.45%,a sensitivity of 99.33%,a specificity of 98.22%,and an F1 score of 99.39%.The proposed B-LQ multiplier achieves 316 LUT and 1.758 ns of latency,while the proposed SP adder achieves 40 LUT,74 IO,and 1.610 ns of delay.Conclusion:Overall,by combining hardware-optimized computation and compression-aware signal processing,the proposed architecture shows an incredibly efficient solution for real-time AF identification in edge and wearable devices.
摘要The paper is devoted to noncommutative formal geometry of a contractive quantum plane,whose spectrum is the union of two copies of the complex plane.It turns out that a formal completion of the Arens-Michael envelope of a contractive quantum plane results in a noncommutative analytic space,whose base topological space is the same spectrum,whereas the structure sheaf is obtained as a certain quantization of the related commutative analytic space.As the basic tool we use the fibered products of the Fréchet sheaves.The related topological homology problems are considered to find out a key link between the transversality relation of the noncommutative sections versus to a left Fréchet module,and noncommutative Taylor spectrum of the module.
基金supported in part by the National Key R&D Program of China(No.2023YFB4704400)in part by the National Natural Science Foundation of China(Nos.U23B2036,U2013201).
摘要In this paper,a distributed Event-Triggered(ET)collision avoidance coordinated control for Quadrotor Unmanned Aerial Vehicles(QUAVs)is proposed based on Virtual Tubes(VTs)with flexible boundaries in the presence of unknown external disturbances.Firstly,VTs are constructed for each QUAV,and the QUAV is restricted into the corresponding VT by the artificial potential field,which is distributed around the boundary of the VT.Thus,the collisions between QUAVs are avoided.Besides,the boundaries of the VTs are flexible by the modification signals,which are generated by the self-regulating auxiliary systems,to make the repulsive force smaller and give more buffer space for QUAVs without collision.Then,a novel ET mechanism is designed by introducing the concept of prediction to the traditional fixed threshold ET mechanism.Furthermore,a disturbance observer is proposed to deal with the adverse effects of the unknown external disturbance.On this basis,a distributed ET collision avoidance coordinated controller is proposed.Then,the proposed controller is quantized by the hysteresis uniform quantizer and then sent to the actuator only at the ET instants.The boundedness of the closed-loop signals is verified by the Lyapunov method.Finally,simulation and experimental results are performed to demonstrate the superiority of the proposed control method.