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Found 5 papers, 1 papers with code
SYQ: Learning Symmetric Quantization For Efficient Deep Neural Networks
An efficient way to reduce this complexity is to quantize the weight parameters and/or activations during training by approximating their distributions with a limited entry codebook.
Quantizing Convolutional Neural Networks for Low-Power High-Throughput Inference Engines
Deep learning as a means to inferencing has proliferated thanks to its versatility and ability to approach or exceed human-level accuracy.
Compressing Low Precision Deep Neural Networks Using Sparsity-Induced Regularization in Ternary Networks
A low precision deep neural network training technique for producing sparse, ternary neural networks is presented.
Inference of Quantized Neural Networks on Heterogeneous All-Programmable Devices
Neural networks have established as a generic and powerful means to approach challenging problems such as image classification, object detection or decision making.
FINN-R: An End-to-End Deep-Learning Framework for Fast Exploration of Quantized Neural Networks
Given a neural network description, the tool optimizes for given platforms, design targets and a specific precision.
Hardware Architecture
