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Found 6 papers, 1 papers with code
Super Efficient Neural Network for Compression Artifacts Reduction and Super Resolution
Video quality can suffer from limited internet speed while being streamed by users.
A Block Minifloat Representation for Training Deep Neural Networks
Training Deep Neural Networks (DNN) with high efficiency can be difficult to achieve with native floating point representations and commercially available hardware.
AddNet: Deep Neural Networks Using FPGA-Optimized Multipliers
Low-precision arithmetic operations to accelerate deep-learning applications on field-programmable gate arrays (FPGAs) have been studied extensively, because they offer the potential to save silicon area or increase throughput.
Monte Carlo Deep Neural Network Arithmetic
We present a novel technique, Monte Carlo Deep Neural Network Arithmetic (MCA), for determining the sensitivity of Deep Neural Networks to quantization in floating point arithmetic. We do this by applying Monte Carlo Arithmetic to the inference computation and analyzing the relative standard deviation of the neural network loss.
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.
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.
