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Graph Hypernetworks (GHN) can predict the parameters of varying unseen CNNarchitectures with surprisingly good accuracy at a fraction of the cost ofiterative optimization. Following these successes, preliminary research hasexplored the use of GHNs to predict quantization-robust parameters for 8-bitand 4-bit quantized CNNs. However, this early work leveraged full-precisionfloat32 training and only quantized for testing. We explore the impact ofquantization-aware training and/or other quantization-based training strategieson quantized robustness and performance of GHN predicted parameters forlow-precision CNNs. We show that quantization-aware training can significantlyimprove quantized accuracy for GHN predicted parameters of 4-bit quantized CNNsand even lead to greater-than-random accuracy for 2-bit quantized CNNs. Thesepromising results open the door for future explorations such as investigatingthe use of GHN predicted parameters as initialization for further quantizedtraining of individual CNNs, further exploration of "extreme bitwidth"quantization, and mixed precision quantization schemes.

GHN-QAT: Training Graph Hypernetworks to Predict Quantization-Robust Parameters of Unseen Limited Precision Neural Networks