Researchers at Soongsil University (Korea) published “A Survey on Efficient Convolutional Neural Networks and Hardware Acceleration.” Abstract: “Over the past decade, deep-learning-based ...

Convolutional neural networks expect a grid that represents the different dimensions of the data they process (e.g., width, height, and color channels of images).

To achieve this, we pose chip floorplanning as a reinforcement learning problem, and develop an edge-based graph convolutional neural network architecture capable of learning rich and transferable ...

Improving Convolutional Neural Networks at the Edge Sept. 15, 2022 Artificial intelligence and more specifically, machine learning, with human augmentation requires a thorough strategy to achieve ...

Finally, convolutional neural networks can be trained end-to-end, allowing gradient descent to simultaneously optimize all of the network’s parameters for performance and faster convergence.