Thanks to the neural network, the researchers now suspect, for example, that the black hole at the center of the Milky Way is spinning almost at top speed. Its rotation axis points to Earth.

Quantized Spiking Neural Networks (QSNNs) offer superior energy efficiency and are well-suited for deployment on resource-limited edge devices. However, limited bit-width weight and membrane potential ...

Post-training quantization (PTQ) has emerged as a practical approach to compress large neural networks, making them highly efficient for deployment. However, effectively reducing these models to their ...

What makes this development especially revolutionary in battery research is the integration of physics-informed principles into neural networks. Traditional neural networks are data-driven models that ...

Star 566 Code Issues Pull requests QKeras: a quantization deep learning library for Tensorflow Keras machine-learning fpga deep-learning tensorflow accelerator keras quantization hardware-acceleration ...

This work introduces SPlus, a new optimizer for neural network training. We developed SPlus from an fundamentally empirical and experimental point of view -- the goal was to make a practical optimizer ...