Quantization breaks memory in recurrent-state write-back for low-precision temporal inference in a GRU encoder–decoder for fluorescence lifetime imaging
Read the original at arxiv.org→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...
Original headline: "When Quantization Breaks Memory: Recurrent-State Write-Back in Low-Precision Temporal Inference"
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- Sep 7, 04:00 UTC arXiv cs.AI lead source When Quantization Breaks Memory: Recurrent-State Write-Back in Low-Precision Temporal Inference