Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive
safely. Given the limited hardware resources, existing 3D perception models are not able to …

Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial
intelligence (AI), including computer vision, natural language processing, and speech …

High-resolution images enable neural networks to learn richer visual representations.
However, this improved performance comes at the cost of growing computational …

3D neural networks are widely used in real-world applications (eg, AR/VR headsets, self-
driving cars). They are required to be fast and accurate; however, limited hardware …

Quantized neural networks typically require smaller memory footprints and lower
computation complexity, which is crucial for efficient deployment. However, quantization …

In this paper, a new adaptive quantization algorithm for generalized posit format is
presented, to optimally represent the dynamic range and distribution of deep neural network …

Deep learning technologies have demonstrated remarkable effectiveness in a wide range of
tasks, and deep learning holds the potential to advance a multitude of applications …

Deep neural networks (DNNs) have demonstrated their effectiveness in a wide range of
computer vision tasks, with the state-of-the-art results obtained through complex and deep …

Uniform quantization is widely taken as an efficient compression method in practical
applications. Despite its merit of having a low computational overhead, uniform quantization …

In recent years, the lottery tickets hypothesis has gained widespread popularity as a means
of network compression. However, the practical application of lottery tickets for hardware …