Transformer-based large language models (LLMs) have achieved great success with the
growing model size. LLMs' size grows by 240× every two years, which outpaces the …

Quantization is a technique to reduce the computation and memory cost of DNN models,
which are getting increasingly large. Existing quantization solutions use fixed-point integer …

Deep learning (DL) models have achieved great success in many application domains. As
such, many industrial companies such as Google and Facebook have acknowledged the …

3D perception in point clouds is transforming the perception ability of future intelligent
machines. Point cloud algorithms, however, are plagued by irregular memory accesses …

Abstract Convolutional Neural Networks (CNNs) play a crucial role in many image
recognition and classification tasks, recommender systems, brain-computer interfaces, etc …

This article proposes a novel hardware accelerator for the inference task with sparse
convolutional neural networks (CNNs) by building a hardware unit to perform Image to …

Deep Neural Network (DNN) inference based on quantized narrow-precision integer data
represents a promising research direction toward efficient deep learning computations on …

Weight sparsity is a promising approach to reducing the model size and computation cost of
convolutional neural networks (CNNs). Nevertheless, non-zero weights often distribute …

多功能一体化系统是电子信息技术领域的重要发展方向之一, 一体化信号处理是其中的关键技术
, 对实现各种功能之间资源共享与高效协同具有重大意义, 同时也从算法到处理架构提出了新的 …

Multi-core neural processing units (NPUs) have emerged to scale the computation capability
of NPUs to efficiently support diverse machine learning tasks. In such multi-core NPUs …