FeNN-DMA: A RISC-V SoC for SNN acceleration

arXiv:2511.00732v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) are a promising, energy-efficient alternative to standard Artificial Neural Networks (ANNs) and are particularly well-suited to

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Biosynthesis cost and catabolic pathway length explain frequency of amino acid utilization as carbon sources by bacteria

Heterotrophic bacteria exhibit remarkable taxonomic and metabolic diversity. Experimental phenotyping of individual strains is often impractical due to the diversity of metabolic strategies, highlighting the need for predictive approaches based on biochemical principles. Here, we demonstrate that bacteria preferentially catabolize amino acids that are energetically inexpensive to synthesize and require shorter degradation pathways. Our results show how general biochemical constraints shape amino acid catabolism across bacterial lineages and provide a framework for integrating microbial physiology into predictive ecological theory.

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