When Quantization Breaks Memory: Recurrent-State Write-Back in Low-Precision Temporal Inference
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arXiv:2609.04490v1 Announce Type: new Abstract: Quantization is widely used to reduce the computational and memory demands of neural-network inference. In recurrent networks, however, the quantized state is stored and returned at the next time step, so the rule used to store that state can alter su…
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- 2026-09-07 04:00 · arXiv cs.AI
When Quantization Breaks Memory: Recurrent-State Write-Back in Low-Precision Temporal Inference