cs.NE · 2026-09-01 · No. 102

Neural and Evolutionary Computing, 2026-09-01.

2 new papers in cs.NE. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

2 entries
  1. 01

    Conjoint Audio-to-Spikes Encoding and Processing for Efficient Neuromorphic Speech Recognition

    Valentin M. Meunier, Amélie Gruel, Pierre Lewden, Adrien F. Vincent, Sylvain Saïghi

    cs.NE · cs.AI · cs.LG · eess.AS

    Obtaining data from neuromorphic sensors and processing it with Spiking Neural Networks is a promising solution to lower the energy cost of artificial intelligence. The current rarity of natively neuromorphic datasets promotes the development of software tools to translate input sensory data into spikes. However, highly bio-mimetic simulators can be challenging to implement on digital hardware. In this work, we evaluate the neuromorphic...

    arxiv.org/abs/2608.30792 · PDF

  2. 02

    Event-Driven Language Models with Sparse Neural Activity for Neuromorphic Hardware

    Simon Richter, Ruhai Lin, Jason Yik, Taylor Kergan, Rui-Jie Zhu, Farshad Moradi, Jason Eshraghian

    cs.NE · cs.LG

    Inference with transformer-based large language models (LLMs) is often limited by the memory-bound KV cache and quadratic attention cost. State-space models (SSMs) mitigate this through linear attention and fixed-size recurrent states, but their large dense linear projections remain computationally expensive even after quantization. We introduce a method that induces sparse neural activity in heavily quantized linear-attention models with...

    arxiv.org/abs/2608.30439 · PDF

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