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Found 5 papers, 2 papers with code
Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference
We study various configurations of pruning during quantization-aware training, which we term quantization-aware pruning, and the effect of techniques like regularization, batch normalization, and different pruning schemes on performance, computational complexity, and information content metrics.
FAT: Training Neural Networks for Reliable Inference Under Hardware Faults
Our experiments show that by injecting faults in the convolutional layers during training, highly accurate convolutional neural networks (CNNs) can be trained which exhibits much better error tolerance compared to the original.
LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput Applications
Deployment of deep neural networks for applications that require very high throughput or extremely low latency is a severe computational challenge, further exacerbated by inefficiencies in mapping the computation to hardware.
Scaling Binarized Neural Networks on Reconfigurable Logic
Binarized neural networks (BNNs) are gaining interest in the deep learning community due to their significantly lower computational and memory cost.
FINN: A Framework for Fast, Scalable Binarized Neural Network Inference
Research has shown that convolutional neural networks contain significant redundancy, and high classification accuracy can be obtained even when weights and activations are reduced from floating point to binary values.
