Search Results for author:
Found 9 papers, 2 papers with code
GPTVQ: The Blessing of Dimensionality for LLM Quantization
In this work we show that the size versus accuracy trade-off of neural network quantization can be significantly improved by increasing the quantization dimensionality.
Pruning vs Quantization: Which is Better?
We provide an extensive comparison between the two techniques for compressing deep neural networks.
FP8 versus INT8 for efficient deep learning inference
We theoretically show the difference between the INT and FP formats for neural networks and present a plethora of post-training quantization and quantization-aware-training results to show how this theory translates to practice.
FP8 Quantization: The Power of the Exponent
We detail the choices that can be made for the FP8 format, including the important choice of the number of bits for the mantissa and exponent, and show analytically in which settings these choices give better performance.
Quantized Sparse Weight Decomposition for Neural Network Compression
In this paper, we introduce a novel method of neural network weight compression.
Cyclical Pruning for Sparse Neural Networks
Current methods for pruning neural network weights iteratively apply magnitude-based pruning on the model weights and re-train the resulting model to recover lost accuracy.
Quantized sparse PCA for neural network weight compression
In this paper, we introduce a novel method of weight compression.
Taxonomy and Evaluation of Structured Compression of Convolutional Neural Networks
The success of deep neural networks in many real-world applications is leading to new challenges in building more efficient architectures.
End-to-end Learning of Cost-Volume Aggregation for Real-time Dense Stereo
We present a new deep learning-based approach for dense stereo matching.
