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Found 6 papers, 2 papers with code
Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples
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 CIFAR-100
It's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teacher
To deal with the performance drop induced by quantization errors, a popular method is to use training data to fine-tune quantized networks.
DANCE: Differentiable Accelerator/Network Co-Exploration
In such circumstances, this work presents DANCE, a differentiable approach towards the co-exploration of the hardware accelerator and network architecture design.
ConCoDE: Hard-constrained Differentiable Co-Exploration Method for Neural Architectures and Hardware Accelerators
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.
Tech Report: One-stage Lightweight Object Detectors
This work is for designing one-stage lightweight detectors which perform well in terms of mAP and latency.
Enabling Hard Constraints in Differentiable Neural Network and Accelerator Co-Exploration
Co-exploration of an optimal neural architecture and its hardware accelerator is an approach of rising interest which addresses the computational cost problem, especially in low-profile systems.
