The paper introduces a lightweight neural network inspired by insect olfaction that classifies raw gas sensor readings using a sparse, high‑dimensional binary representation. It operates online, handling random sequences of single gases and binary mixtures, and dynamically incorporates new samples while providing class‑level frequency estimates to differentiate familiar from novel stimuli. The architecture, built with binary weights and Hebbian local learning, outperforms a linear SVM on mixtures, matches an optimized nonlinear SVM, and was demonstrated on the TEXEL neuromorphic chip with 74.6% accuracy, 482 nJ per inference, and 27 µW static power.
By Ton Juny Pina, Hugh Greatorex, Elisabetta Chicca
arXiv:2608. 03319v1 Announce Type: cross Abstract: Future integrated sensing and communication (ISAC) architectures separate the sensing entity (SE) that acquires measurements from the sensing function (SF) that performs inference, creating a need for compact, task-oriented feedback on the SE-SF interface.
By Shiv Shankar, Radha Krishna Ganti, J Klutto Milleth
arXiv:2606. 08161v1 Announce Type: new Abstract: As capacitance extraction accuracy of rule-based pattern matching becomes difficult to sustain at advanced nodes, a growing trend emerges to develop deep-learning-based 2D capacitance models.
By Jiechen Huang, Hector R. Rodriguez, Dingcheng Yang, Zuochang Ye, Yibo Lin, Wenjian Yu
arXiv:2608. 07192v1 Announce Type: new Abstract: Low-resolution infrared (IR) array sensors represent an interesting solution for privacy-preserving human sensing in embedded systems.
By Beatrice Alessandra Motetti, Tanguy Dugas du Villard, Matteo Risso, Alessio Burrello, Francesco Daghero, Enrico Macii, Massimo Poncino, Marco Castellano, Alfio Basile, Daniele Jahier Pagliari
arXiv:2609.13947v1 Announce Type: cross
Abstract: In-sensor computing reduces the cost of transmitting high-resolution image data by performing early-stage processing near the sensor. However, the lo...
By Chengwei Zhou, Abu Masum, Xuming Chen, Mehran Moghadam, Sreetama Sarkar, Arnab Sanyal, Md Abdullah-Al Kaiser, M. Hassan Najafi, Sercan Aygun, Gourav Datta
The paper presents a novel approach to monitoring industrial anaerobic co‑digestion by combining a generative LSTM‑beta‑VAE with a multi‑output LSTM soft sensor. By synthesising physically valid sequences of unstable operation from a year of plant data, the method augments sparse datasets and enables simultaneous prediction of key stability indicators such as pH, volatile acid ratio, and biogas production. The augmented model outperforms standard algorithms and unaugmented LSTMs, offering robust early‑warning capabilities for bioprocess stability.
By Rohit Murali, Benaissa Dekhici, Tao Chen, Dongda Zhang, Michael Short
The paper introduces a lightweight neural network inspired by insect olfaction that classifies raw gas sensor readings using a sparse, high‑dimensional binary representation. It operates online, can be recalibrated from limited data, and outperforms linear SVMs on gas mixtures while matching optimized nonlinear SVMs. The architecture uses only binary weights and Hebbian learning, enabling deployment on ultra‑low‑power neuromorphic hardware, as demonstrated on the TEXEL chip with 74.6% accuracy at 27 µW static power.
arXiv:2607. 07953v1 Announce Type: cross Abstract: Self-attention lets each token retrieve information from the full context, but its quadratic cost in sequence length limits training and inference at long context.
By Tommaso Cerruti, Tim Rieder, George Rowlands, Lingfeng Jin, Imanol Schlag
arXiv:2606. 03748v1 Announce Type: cross Abstract: Real-time vision demands models that are accurate, efficient, and simple to deploy across diverse hardware.
By Glenn Jocher, Jing Qiu, Mengyu Liu, Shuai Lyu, Fatih Cagatay Akyon, Muhammet Esat Kalfaoglu
arXiv:2605. 18848v3 Announce Type: replace Abstract: This paper introduces Exact Linear Attention (ELA), a mechanism that achieves linear computational complexity for Transformer attention by exploiting the exact decomposition property of kernel functions, thereby eliminating approximation error.
By Weinuo Ou
arXiv:2606. 23825v1 Announce Type: cross Abstract: Efficient small object detection is bottlenecked by the inherent feature scarcity of tiny targets, which is further aggravated by operations of spatial-domain detectors that indiscriminately discard critical high-frequency details.
By Yuhan Rui, Shihan Qiao, Yibin Lou, Mingxi Yu, Yutong Wan, Yanqiao Chen, Dongsheng Hou, Zhen Cao, Athena Zhuoming Zhong, Qi Hao
Low-resolution infrared (IR) array sensors represent an interesting solution for privacy-preserving human sensing in embedded systems. In this letter, we describe a smart multi-pixel IR sensor integrating a 16$\times$16 thermal MOSFET (TMOS) array and a RISC-V microcontroller extended with low-precision SIMD instructions, capable of on-device learning and continual adaptation for pose and gesture recognition tasks under tight memory and power constraints ($<$32kB on-chip memory, $\approx$1.