The paper demonstrates that quadratic integrate-and-fire (QIF) neurons are superior to leaky integrate-and-fire (LIF) neurons for gradient descent training in spiking neural networks because their con…

The paper systematically characterizes the fault response of the Intel NCS2 accelerator to electromagnetic fault injection, revealing a major degradation mode that is undetectable by standard inferenc…

The paper introduces FHE-DiCSNN, a novel framework that uses the TFHE scheme to enable secure and efficient computation on Spiking Neural Networks (SNNs), achieving high accuracy and fast inference ti…

The paper proposes a Ferroelectric Charge-Domain Compute Cell (FCDC) using HZO memcapacitors to perform attention computation, achieving significant energy efficiency gains, especially for long-reside…

This paper proposes and validates a novel hardware architecture, ITP-STDP, to significantly reduce the energy consumption and hardware overhead associated with training Spiking Neural Networks (SNNs).

The paper proposes a constant-time implementation methodology for activation functions on microcontrollers to prevent timing side-channel attacks during embedded neural-network inference.

The paper introduces Cross-Model Neuron Transfer (CNT), a post-hoc method that efficiently transfers safety-oriented functionalities between different large language models by transferring minimal sub…

This paper investigates a novel vulnerability in tactile sensing by demonstrating that targeted Electromagnetic Interference (EMI) can induce strong, misleading 'phantom forces' in Hall-effect fingert…

The paper introduces SpikeWFM, a novel hybrid architecture combining spiking neural networks (SNNs) and transformers, which significantly improves the robustness and accuracy of wireless foundation mo…

The paper systematically evaluates 27 Spiking Neural Network (SNN) configurations to determine the optimal combination of neuron model and spike encoding scheme for network intrusion detection, findin…

The paper evaluates 27 different Spiking Neural Network (SNN) configurations to determine the optimal design for network intrusion detection, finding that the LeakyParallel neuron combined with latenc…

This paper presents BenDi, an energy-efficient quasi-stochastic systolic architecture for bioelectronic systems on the edge.

The paper introduces memorywire, a vendor-neutral JSON-Schema wire format and reference implementation designed to standardize and govern memory operations across disparate agent-memory frameworks.

The paper introduces a physics-informed active learning framework to optimize GaN tri-gate FinFETs for vertical power delivery, identifying a multi-fin device (D1) that significantly outperforms a sin…

The paper introduces partial multi-neuron relaxation, a novel verification technique that selectively computes tight linear bounds for a small subset of neurons to improve the efficiency and tightness…

The paper proposes constant depth threshold circuits for efficiently detecting epistasis by calculating the relative frequencies of all dataset combinations using specialized hardware architectures.

The paper proposes SecurePix, a compact CMOS-compatible pixel architecture that achieves true in-pixel encryption using FeFETs, demonstrating strong image security and low power overhead.