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Found 8 papers, 8 papers with code
Linear Log-Normal Attention with Unbiased Concentration
Transformer models have achieved remarkable results in a wide range of applications.
Rotation Invariant Quantization for Model Compression
Post-training Neural Network (NN) model compression is an attractive approach for deploying large, memory-consuming models on devices with limited memory resources.
Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming
Instead, these methods only use the calibration set to set the activations' dynamic ranges.
Robust Quantization: One Model to Rule Them All
Neural network quantization methods often involve simulating the quantization process during training, making the trained model highly dependent on the target bit-width and precise way quantization is performed.
Post training 4-bit quantization of convolutional networks for rapid-deployment
Convolutional neural networks require significant memory bandwidth and storage for intermediate computations, apart from substantial computing resources.
Loss Aware Post-training Quantization
We show that with more aggressive quantization, the loss landscape becomes highly non-separable with steep curvature, making the selection of quantization parameters more challenging.
ACIQ: Analytical Clipping for Integer Quantization of neural networks
We analyze the trade-off between quantization noise and clipping distortion in low precision networks.
Post-training 4-bit quantization of convolution networks for rapid-deployment
Convolutional neural networks require significant memory bandwidth and storage for intermediate computations, apart from substantial computing resources.
