Post-training quantization remains the standard optimization playbook for squeezing neural networks onto edge hardware. In feedforward architectures, rounding weights and activations introduces bounded, localized noise. In recurrent neural networks, however, that assumption collapses: the internal state is persistently written back and fed into successive inference steps. When standard compression rules compress these temporal states, rounding is no longer passive noise—it fundamentally breaks the network's computational trajectory over long sequences.
The Breakdown of Temporal Updates
In a study from Rensselaer Polytechnic Institute, researchers Ismail Erbas, Xavier Intes, and Vikas Pandey systematically isolated how recurrent-state write-back—the explicit rule used to discretize and store recurrent memory across steps—distorts sequential inference. Testing a compact GRU encoder–decoder deployed for high-noise fluorescence lifetime imaging, the team measured parameter estimation errors for biological lifetime decay constants τ1 and τ2.
With model weights held strictly fixed, replacing full-precision state propagation with deterministic 4-bit recurrent-state storage caused estimation errors to surge by approximately 70-fold for τ1 and nearly 300-fold for τ2.
Failure occurs when repeated small updates remain below the write threshold, leaving the stored state nearly fixed while the network continues to propose change.
Because the recurrent state functions as a dynamic accumulator, coarse quantization acts as an impermeable barrier. When subtle temporal shifts fall beneath the rounding threshold, the state value never increments. The state trajectory stalls, effectively erasing sequence history despite continuous computational effort from the network.
Precision Sweeps and Architecture Dynamics
Crucially, precision sweeps showed that recurrent reliability does not scale monotonically with bit width; under specific write-back conditions, arbitrarily increasing state precision actually worsened fixed recurrent outputs by amplifying drift. Accuracy, however, was restored without retraining by introducing error-feedback mechanisms, residual accumulation, or direction memory to carry sub-threshold residual updates across time steps.
The authors confirmed identical failure modes on independently trained LSTM architectures. Coarse write-back reproduced the same trajectory freeze, and state-specific ablation revealed that LSTM cell states exhibit significantly higher sensitivity to quantization drift than hidden states.
For engineering teams deploying streaming models and edge architectures, the takeaway is clear: recurrent states cannot be treated as static activation tensors. Applying naive post-training quantization to stateful models without dynamic error feedback guarantees systematic memory erosion over extended deployment horizons.