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:2608.23101v1 Announce Type: cross
Abstract: Recent progress in the field of TinyML has demonstrated that low-power hardware based on microcontrollers can achieve bird species monitoring in real...
By Nathan Duboisset, Zhaolan Huang, Felix Bie{\ss}mann, Roudy Dagher, Antoine Lavandier, Emmanuel Baccelli
arXiv:2606. 24075v1 Announce Type: cross Abstract: Although deep learning-based methods can achieve high accuracy in automatic modulation recognition (AMR) tasks, their high computational cost makes it difficult to strike a balance between accuracy and power consumption, thereby limiting their application on resource-constrained platforms.
By Xiaohu Li, Chongxiao Qu, Caiyong Lin, Chenxiao Dou, Wei Hua
LDAC‑Net is a learnable multi‑lag differencing attention‑convolution network designed for drift‑robust recognition with low‑cost metal‑oxide (MOX) gas sensors. It replaces fixed first‑order temporal differencing with a front‑end that applies window‑conditioned statistical affine normalisation and learnable multi‑lag differencing, followed by an attention‑convolution backbone that captures both local transients and long‑range temporal dependencies. On the 50‑class SmellNet‑Base task, LDAC‑Net attains 68.2 % top‑1 accuracy, outperforming the best fixed‑lag model by about 14 percentage points and a raw‑input Transformer by over 30 points, and it also transfers well to other benchmarks such as SmellNet‑Mixtures and eNose‑Drift.
By Xin Zhang, Liangxiu Han, Yue Shi, Tam Sobeih
arXiv:2607. 05590v1 Announce Type: cross Abstract: Implantable brain-computer interfaces require on-node spike sorting to reduce telemetry bandwidth and power while maintaining reliable neural decoding.
By Binyi Ren, Luca M. Meyer, Majid Zamani
arXiv:2608. 08479v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) offer a promising pathway to energy-efficient AI and brain-inspired computing.
By Prasanna Date, Kevin Zhu, Shruti Kulkarni, Ashish Gautam, Chathika Gunaratne, Robert Patton, Tyler Nitzsche, Ian Mulet, Zachary Johnson-Scott, Addison Helms, Duncan Rowden, Simon Weston, Maryam Parsa, Catherine Schuman, Thomas Potok