arXiv subjects

Wantong Li

Publications and source records attributed to Wantong Li.

9 recordsLinked to original sources

arXiv2026

Gram-Space: Structure-Preserving Codebook Compression for Memory-Efficient Neuro-Symbolic AI

Vector symbolic architectures (VSA) are widely used for reasoning in neuro-symbolic (NeSy) AI, yet high-dimensional codebooks often create severe memory bottlenecks that limit scalability and deployment. In this paper, we propose Gram-Space, a compression framework that applies Gram-Schmidt orthogonalization to represent codebook vectors in a compact orthonormal coordinate system. Gram-Space preserves the dot-product structure required by matrix-based VSA operators, which supports numerically equivalent execution of matrix similarity, probability vectorization, and attention score computations. We provide a correctness analysis showing that inner products are preserved under the orthonormal basis representation. Using modern GPU hardware, we benchmark the Gram-Space framework on standard neuro-symbolic reasoning datasets. Experimental evaluations across state-of-the-art VSA models show that Gram-Space reduces model-level GPU memory usage by up to 15.75x and improves inference latency by up to 3.62x. Profiling results further indicate that Gram-Space reduces allocation-heavy overhead in codebook-associated stages and improves hardware utilization for NeSy workloads.

cs.LG↗

arXiv2026

DarkFlow: Hierarchical Digital SiPM Architecture with Low-Loss Dataflow Readout for Dark Matter Detection

Direct dark matter detection experiments require large-scale photon sensing arrays with hundreds of thousands of synchronized readout channels. Silicon photomultipliers (SiPMs) have emerged as a leading candidate for these detectors due to their high integration density, low bias voltage, and superior radiopurity. However, existing digital SiPM readout architectures struggle to simultaneously preserve nanosecond-level temporal resolution for sparse scintillation events and sustain data integrity during high-intensity photon bursts. We propose DarkFlow, a hierarchical digital SiPM architecture that features local data aggregation, compact relative-time encoding, consumer-driven backpressure, and occupancy-aware eDRAM burst buffering within a unified dataflow framework. We show that DarkFlow maintains ultra-low packet loss at billion-photon event rates, where conventional architectures can exceed 80% data loss. Besides, the occupancy-aware refresh achieves a 2.14x improvement in effective refresh rate over conventional global refresh. Hardware evaluation in GlobalFoundries 22nm node confirms that the digital readout datapath accounts for less than 0.86% of the detector area and complies with the strict power budget in liquid argon environments.

physics.ins-det↗

arXiv2026

ThermoPix: A High-Spatial-Resolution ElectronicPhotonic Temperature Sensor Array With Microsecond Row Readout

This paper presents ThermoPix, a CMOS-compatible electronic-photonic architecture for high-spatial-resolution temperature sensing. The proposed system converts temperature-induced wavelength shifts in a photonic interferometric sensor into timing information that can be processed by CMOS circuitry. We use a valley photonic crystal Mach-Zehnder interferometer (VPCMZI) as the sensing element, whose temperature-dependent spectral response is detected using an integrated waveguide photodetector and translated into a time-varying photocurrent. A CMOS readout circuit employing a phase-transition-material device performs threshold detection and generates a timing signal corresponding to the temperature-dependent crossing event. Circuit-level simulations demonstrate a temperature sensitivity of 3.15 ns/K, a row readout time of 2 us, and a sensing power-delay product (PDP) of 0.152 fJ. The required optical power per photonic cell is 150 nW, enabling energy-efficient array operation without requiring cooling or special environmental arrangements. We also present alternative photonic layer architectures for optical power distribution across the array. In one approach, we use different tap ratios along the row, while the other uses identical tap ratios with bidirectional excitation. The resulting average photonic cell pitches are 23.26 um and 38.52 um, respectively. The proposed ThermoPix architecture therefore provides a scalable platform for integrated temperature sensing arrays that combine photonic sensing elements with CMOS-compatible timing-based readout.

cs.ET↗

arXiv2026

Overmind NSA: A Unified Neuro-Symbolic Computing Architecture with Approximate Nonlinear Activations and Preemptive Memory Bypass

Neuro-symbolic AI is gaining traction in domains such as large language models, scientific discovery, and autonomous systems due to its ability to combine perception with structured reasoning. However, its deployment is often constrained by high memory demands, diverse computation patterns, and complex hardware requirements. Existing hardware platforms struggle with large on-chip memory overheads, frequent pipeline stalls, limited I/O bandwidth, and inefficient handling of nonlinear operations. To address these key computational bottlenecks, we propose Overmind, a unified neuro-symbolic architecture with cross-layer optimizations. Overmind tackles these core bottlenecks through Pad\'e approximations for universal nonlinear functions, preemptive memory bypass that eliminates costly on-chip caches, and a complete software stack that optimizes model deployment. By reconfiguring the Pad\'e orders for approximating nonlinear functions, we also demonstrate adaptive accuracy-performance scaling. Overmind achieves an energy efficiency of 8.1 TOPS/W and a throughput of 410 GOPS for mixed neuro-symbolic workloads with minimal model accuracy loss. Compared to existing solutions, Overmind improves performance and efficiency with significantly fewer hardware resources.

cs.AR↗

arXiv2026

Lightweight True In-Pixel Encryption with FeFET Enabled Pixel Design for Secure Imaging

Ensuring end-to-end security in image sensors has become essential as visual data can be exposed through multiple stages of the imaging pipeline. Advanced protection requires encryption to occur before pixel values appear on any readout lines. This work introduces a secure pixel sensor (SecurePix), a compact CMOS-compatible pixel architecture that performs true in-pixel encryption using a symmetric key realized through programmable, non-volatile multidomain polarization states of a ferroelectric field-effect transistor. The pixel and array operations are designed and simulated in HSPICE, while a 45 nm CMOS process design kit is used for layout drawing. The resulting layout confirms a pixel pitch of 2.33 x 3.01 um^2. Each pixel's non-volatile programming level defines its analog transfer characteristic, enabling the photodiode voltage to be converted into an encrypted analog output within the pixel. Full-image evaluation shows that ResNet-18 recognition accuracy drops from 99.29 percent to 9.58 percent on MNIST and from 91.33 percent to 6.98 percent on CIFAR-10 after encryption, indicating strong resistance to neural-network-based inference. Lookup-table-based inverse mapping enables recovery for authorized receivers using the same symmetric key. Based on HSPICE simulation, the SecurePix achieves a per-pixel programming power-delay product of 17 uW us and a per-pixel sensing power-delay product of 1.25 uW us, demonstrating low-overhead hardware-level protection.

cs.CV↗

arXiv2025

Enhanced Hybrid Temporal Computing Using Deterministic Summations for Ultra-Low-Power Accelerators

This paper presents an accuracy-enhanced Hybrid Temporal Computing (E-HTC) framework for ultra-low-power hardware accelerators with deterministic additions. Inspired by the recently proposed HTC architecture, which leverages pulse-rate and temporal data encoding to reduce switching activity and energy consumption but loses accuracy due to its multiplexer (MUX)-based scaled addition, we propose two bitstream addition schemes: (1) an Exact Multiple-input Binary Accumulator (EMBA), which performs precise binary accumulation, and (2) a Deterministic Threshold-based Scaled Adder (DTSA), which employs threshold logic for scaled addition. These adders are integrated into a multiplier accumulator (MAC) unit supporting both unipolar and bipolar encodings. To validate the framework, we implement two accelerators: a Finite Impulse Response (FIR) filter and an 8-point Discrete Cosine Transform (DCT)/iDCT engine. Results on a 4x4 MAC show that, in unipolar mode, E-HTC matches the RMSE of state-of-the-art Counter-Based Stochastic Computing (CBSC) MAC, improves accuracy by 94% over MUX-based HTC, and reduces power and area by 23% and 7% compared to MUX-based HTC and 64% and 74% compared to CBSC. In bipolar mode, E-HTC MAC achieves 2.09% RMSE -- an 83% improvement over MUX-based HTC -- and approaches CBSC's 1.40% RMSE with area and power savings of 28% and 43% vs. MUX-based HTC and about 76% vs. CBSC. In FIR experiments, both E-HTC variants yield PSNR gains of 3--5 dB (30--45% RMSE reduction) while saving 13% power and 3% area. For DCT/iDCT, E-HTC boosts PSNR by 10--13 dB (70--75% RMSE reduction) while saving area and power over both MUX- and CBSC-based designs.

cs.AR↗

arXiv2025

Multi-Domain FeFET-Based Pixel for In-Sensor Multiply-and-Accumulate Operations

This paper presents an FeFET-based active pixel sensor that performs in-sensor multiply-and-accumulate (MAC) operations by leveraging the multi-domain polarization states of ferroelectric layers. The proposed design integrates a programmable FeFET into a 3-transistor pixel circuit, where the FeFET's non-volatile conductance encodes the weight, and the photodiode voltage drop encodes the input. Their interaction generates an output current proportional to the product, enabling in-pixel analog multiplication. Accumulation is achieved by summing output currents along shared column lines, realizing full MAC functionality within the image sensor array. Extensive HSPICE simulations, using 45 nm CMOS models, validate the operation and confirm the scalability of the design. This compact and power-efficient architecture minimizes data movement, making it ideal for real-time edge computing, neuromorphic vision, and secure sensing applications.

eess.IV↗

arXiv2025

Diagnosing syndromes of biosphere-atmosphere-socioeconomic change

It is increasingly recognized that the multiple and systemic impacts of Earth system change threaten the prosperity of society through altered land carbon dynamics, freshwater variability, biodiversity loss, and climate extremes. For example, in 2022, there are about 400 climate extremes and natural hazards worldwide, resulting in significant losses of lives and economic damage. Beyond these losses, comprehensive assessment on societal well-being, ecosystem services, and carbon dynamics are often understudied. The rapid expansion of geospatial, atmospheric, and socioeconomic data provides an unprecedented opportunity to develop systemic indices to account for a more comprehensive spectrum of Earth system change risks and to assess their socioeconomic impacts. We propose a novel approach based on the concept of syndromes that can integrate synchronized changes in biosphere, atmosphere, and socioeconomic trajectories into distinct co-evolving phenomena. While the syndrome concept was applied in policy related to environmental conservation, it has not been deciphered from systematic data-driven approaches capable of providing a more comprehensive diagnosis of anthropogenic impacts. By advocating interactive dimensionality reduction approaches, we can identify key interconnected socio-environmental changes as syndromes from big data. We recommend future research tailoring syndromes by incorporating granular data, particularly socio-economic, into dimensionality reduction at different spatio-temporal scales to better diagnose regional-to-global atmospheric and environmental changes that are relevant for socioeconomic changes.

physics.geo-ph↗

arXiv2018

The dynamics of a Fisher-KPP nonlocal diffusion model with free boundaries

We introduce and study a class of free boundary models with "nonlocal diffusion", which are natural extensions of the free boundary models in Du and Lin [17] and elsewhere, where "local diffusion" is used to describe the population dispersal, with the free boundary representing the spreading front of the species. We show that this nonlocal problem has a unique solution defined for all time, and then examine its long-time dynamical behavior when the growth function is of Fisher-KPP type. We prove that a spreading-vanishing dichotomy holds, though for the spreading-vanishing criteria significant differences arise from the well known local diffusion model in Du and Lin [17].

math.AP↗