To deal with the performance drop induced by quantization errors, a popular method is to use training data to fine-tune quantized networks.
We find that this is often insufficient to capture the distribution of the original data, especially around the decision boundaries.
Ranked #1 on Data Free Quantization on CIFAR10
To handle the hard constraint problem of differentiable co-exploration, we propose ConCoDE, which searches for hard-constrained solutions without compromising the global design objectives.
In such circumstances, this work presents DANCE, a differentiable approach towards the co-exploration of the hardware accelerator and network architecture design.