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: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: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
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