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Found 4 papers, 1 papers with code
Adaptive Differential Filters for Fast and Communication-Efficient Federated Learning
Federated learning (FL) scenarios inherently generate a large communication overhead by frequently transmitting neural network updates between clients and server.
ECQ$^{\text{x}}$: Explainability-Driven Quantization for Low-Bit and Sparse DNNs
The remarkable success of deep neural networks (DNNs) in various applications is accompanied by a significant increase in network parameters and arithmetic operations.
FantastIC4: A Hardware-Software Co-Design Approach for Efficiently Running 4bit-Compact Multilayer Perceptrons
With the growing demand for deploying deep learning models to the "edge", it is paramount to develop techniques that allow to execute state-of-the-art models within very tight and limited resource constraints.
Learning Sparse & Ternary Neural Networks with Entropy-Constrained Trained Ternarization (EC2T)
To address this problem, we propose Entropy-Constrained Trained Ternarization (EC2T), a general framework to create sparse and ternary neural networks which are efficient in terms of storage (e. g., at most two binary-masks and two full-precision values are required to save a weight matrix) and computation (e. g., MAC operations are reduced to a few accumulations plus two multiplications).
