Researchers introduced a method called recurrent-state write-back to address memory breakdowns caused by quantization in low-precision temporal inference for recurrent neural networks. The technique modifies how quantized states are stored and returned at each time step, preventing errors from accumulating over time. This advance could improve the reliability of quantized recurrent models in resource-constrained deployments.
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 subsequent computations. Here, we introduce recurre
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