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Found 7 papers, 1 papers with code
Efficient and Effective Methods for Mixed Precision Neural Network Quantization for Faster, Energy-efficient Inference
Using EAGL and ALPS for layer precision selection, full-precision accuracy is recovered with a mix of 4-bit and 2-bit layers for ResNet-50, ResNet-101 and BERT-base transformer networks, demonstrating enhanced performance across the entire accuracy-throughput frontier.
Learned Step Size Quantization
Deep networks run with low precision operations at inference time offer power and space advantages over high precision alternatives, but need to overcome the challenge of maintaining high accuracy as precision decreases.
Ranked #1 on
Model Compression
on ImageNet
Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference
Therefore, we (a) reduce solution distance by starting with pretrained fp32 precision baseline networks and fine-tuning, and (b) combat gradient noise introduced by quantization by training longer and reducing learning rates.
Structured Convolution Matrices for Energy-efficient Deep learning
We derive a relationship between network representation in energy-efficient neuromorphic architectures and block Toplitz convolutional matrices.
Deep neural networks are robust to weight binarization and other non-linear distortions
Recent results show that deep neural networks achieve excellent performance even when, during training, weights are quantized and projected to a binary representation.
Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing
Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks.
Backpropagation for Energy-Efficient Neuromorphic Computing
Solving real world problems with embedded neural networks requires both training algorithms that achieve high performance and compatible hardware that runs in real time while remaining energy efficient.
