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Technische Universit ät Dresden, Centre for Tactile Internet
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Achieving up to 4.5 TOPS while consuming less than 250 mW, the SpiNNaker2 chip redefines energy efficiency in brain-inspired computing.
Event-based neural decoding achieves superior task performance while drastically reducing energy consumption and latency, revolutionizing neuroprosthetic control.
Surprisingly, even with carefully designed retrieval pipelines, smaller language models still fall short of larger models on complex scientific reasoning tasks, suggesting that retrieval and model scale are complementary, not interchangeable.
