Autonomous driving services depends on active sensing from modules such as camera,
LiDAR, radar, and communication units. Traditionally, these modules process the sensed …

Channel (or 3D filter) pruning serves as an effective way to accelerate the inference of
neural networks. There has been a flurry of algorithms that try to solve this practical problem …

The brain-inspired and event-driven Spiking Neural Network (SNN) aims at mimicking the
synaptic activity of biological neurons, which transmits binary spike signals between network …

Despite the tremendous progress on data-free knowledge distillation (DFKD) based on
synthetic data generation, there are still limitations in diverse and efficient data synthesis. It …

Zero-shot quantization (ZSQ) is promising for compressing and accelerating deep neural
networks when the data for training full-precision models are inaccessible. In ZSQ, network …

Data-free knowledge distillation is able to utilize the knowledge learned by a large teacher
network to augment the training of a smaller student network without accessing the original …

In the last decade, many deep learning models have been well trained and made a great
success in various fields of machine intelligence, especially for computer vision and natural …

Model quantization has emerged as an indispensable technique to accelerate deep
learning inference. While researchers continue to push the frontier of quantization …