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2 results for “in-memory”
Noise-aware training for analog hardware: accuracy collapses at a threshold rather than degrading smoothly [D]
A researcher conducted an empirical experiment showing that analog in-memory AI hardware exhibits abrupt accuracy collapse under weight noise—rather than gradual degradation—and that noise-aware training shifts the failure threshold, raising questions about optimization strategies for hardware-specific robustness.
Aug 10, 2026
Mapping with In-Memory Layers to Reduce LLM Overload
A Hacker News thread titled 'Mapping with In-Memory Layers to Reduce LLM Overload' contains user comments discussing an unspecified technical approach to optimizing large language model inference, but no article, study, or source material is provided.
Jul 6, 2026