Efficient deployment of deep neural networks (DNNs) emerges with the exploding demand
for artificial intelligence on edge devices. Mixed-precision inference with both compressed …

With the continuous development of deep learning, Deep Convolutional Neural Network
(DCNN) has attracted wide attention in the industry due to its high accuracy in image …

Network performance has contributed fundamentally to the growth of supercomputing over
the past decades. In parallel, High Performance Computing (HPC) peak performance has …

An efficient on-chip learning method based on neuron multiplexing is proposed in this paper
to address the limitations of traditional on-chip learning methods, including low resource …

To enhance the efficiency of image-kernel convolution operations in convolutional neural
networks, we introduce a Vector Systolic Array Accelerator with adaptable lane-width. This …

Deep learning methods, applied to solve complex tasks, are increasingly in demand across
various industries. However, to reach this, the computational operations depend on …