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Found 10 papers, 1 papers with code
EncodingNet: A Novel Encoding-based MAC Design for Efficient Neural Network Acceleration
In this new design, the multipliers are replaced by simple logic gates to project the results onto a wide bit representation.
Class-Aware Pruning for Efficient Neural Networks
The filters that are only important for a few number of classes are removed.
OplixNet: Towards Area-Efficient Optical Split-Complex Networks with Real-to-Complex Data Assignment and Knowledge Distillation
In conventional ONNs, light amplitudes are modulated at the input and detected at the output.
Early-Exit with Class Exclusion for Efficient Inference of Neural Networks
Deep neural networks (DNNs) have been successfully applied in various fields.
Logic Design of Neural Networks for High-Throughput and Low-Power Applications
However, under a given area constraint, the number of MAC units in such platforms is limited, so MAC units have to be reused to perform MAC operations in a neural network.
Computational and Storage Efficient Quadratic Neurons for Deep Neural Networks
Deep neural networks (DNNs) have been widely deployed across diverse domains such as computer vision and natural language processing.
PowerPruning: Selecting Weights and Activations for Power-Efficient Neural Network Acceleration
To address this challenge, we propose PowerPruning, a novel method to reduce power consumption in digital neural network accelerators by selecting weights that lead to less power consumption in MAC operations.
Class-based Quantization for Neural Networks
In the proposed method, the importance score of each filter or neuron with respect to the number of classes in the dataset is first evaluated.
SteppingNet: A Stepping Neural Network with Incremental Accuracy Enhancement
In such platforms, neural networks need to provide acceptable results quickly and the accuracy of the results should be able to be enhanced dynamically according to the computational resources available in the computing system.
CorrectNet: Robustness Enhancement of Analog In-Memory Computing for Neural Networks by Error Suppression and Compensation
These acceleration platforms rely on analog properties of the devices and thus suffer from process variations and noise.
