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Found 5 papers, 0 papers with code
Interleaved Composite Quantization for High-Dimensional Similarity Search
A method often used to reduce this computational cost is quantization of the vector space and location-based encoding of the dataset vectors.
TOCO: A Framework for Compressing Neural Network Models Based on Tolerance Analysis
Neural network compression methods have enabled deploying large models on emerging edge devices with little cost, by adapting already-trained models to the constraints of these devices.
Efficient Large-Scale Approximate Nearest Neighbor Search on OpenCL FPGA
The proposed method significantly outperforms state-of-the-art methods on CPU and GPU for high dimensional nearest neighbor queries on billion-scale datasets in terms of query time and accuracy regardless of the batch size.
DNN Model Compression Under Accuracy Constraints
In this paper, we propose a technique that directly minimizes both the model complexity and the changes in the loss function.
Adaptive Quantization of Neural Networks
The optimization problem at the core of this method iteratively uses the loss function gradient to determine an error margin for each parameter and assigns it a precision accordingly.
