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
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 explores how the number of bird species (target classes) affects the compressibility of neural networks for passive acoustic monitoring on microcontroller units (MCUs). By training and compressing models with varying class counts, the authors show that significant compression can be achieved with minimal performance loss. They also benchmark different hardware platforms and assess the feasibility of deploying energy‑autonomous monitoring devices.
By Nina Brolich, Simon Geis, Maximilian Kasper, Alexander Barnhill, Axel Plinge, Dominik Seu{\ss}
arXiv:2608.30792v1 Announce Type: cross
Abstract: Obtaining data from neuromorphic sensors and processing it with Spiking Neural Networks is a promising solution to lower the energy cost of artificia...
By Valentin M. Meunier, Am\'elie Gruel, Pierre Lewden, Adrien F. Vincent, Sylvain Sa\"ighi
The paper introduces an Embodied Neurocomputation framework that tackles the challenge of encoding and decoding between silicon computers and biological neural networks (BNNs). Using a large‑scale parameter sweep, the authors optimized over 1,300 encoding configurations for a BNN agent navigating an odor‑style gradient in a simulated grid‑world, running more than 4,000 hours of real‑time interactions. Twelve configurations consistently outperformed silicon‑based DQN agents within the same interaction budget, demonstrating the framework’s potential for scalable, goal‑oriented learning with BNNs.
By Johnson Zhou, Daniel Tanneberg, Forough Habibollahi, Alon Loeffler, Kiaran Lawson, Valentina Baccetti, Kwaku Dad Abu-Bonsrah, Candice Desouza, Finn Doensen, Bradley Watmuff, Daria Kornienko, Azin Azadi, Justin Leigh Bourke, Bernhard Sendhoff, Brett J. Kagan
arXiv:2608.21764v1 Announce Type: cross
Abstract: Event-based vision has emerged as a promising paradigm for energy-aware artificial intelligence (AI), offering sparse, low-latency visual signals tha...
By Riadul Islam, Joey Mule, Dhandeep Challagundla, Shahmir Rizvi, Sean Carson, Rachit Saini
The paper introduces the Sparse-Activation-ReLU (SAR) layer, a single‑step neural operator that promotes activation sparsity without surrogate‑gradient training and is compatible with event‑based computing. In a trunk‑based NOMAD architecture, SAR improves the combined Latency‑Error‑Energy (LEE) metric by over fivefold compared to Variable Spiking Neuron (VSN) and Leaky Integrate‑and‑Fire (LIF) models. Additional techniques such as synthetic knowledge distillation, a ReLU‑based spiking loss, and graph‑neighbor thresholding further reduce LEE and L2 error on the Heat Exchanger dataset, advancing energy‑efficient virtual sensing for edge deployment.
By William Howes, Farid Ahmed, Syed Bahauddin Alam