PointAcc is a novel point cloud deep learning accelerator. It introduces a configurable sorting-based mapping unit that efficiently supports diverse operations in point cloud networks. PointAcc further exploits simplified caching and layer fusion specialized for point cloud models, effectively reducing the DRAM access.

SPVNAS enhances Point-Voxel Convolution in large-scale outdoor scenes with sparse convolutions. With 3D Neural Architecture Search (3D-NAS), it efficiently and effectively searches the optimal 3D neural network architecture under a given resource constraint.

We develop a graph neural network and reinforcement learning based method for analog circuit transistor sizing.

APQ is an efficient AutoML framework for joint optimization of neural architecture, pruning, and quantization.

Hardware Accelerator for Sparse Matrix-Matrix Multiplication (SpGEMM)

HAT NAS framework leverages the hardware feedback in the neural architecture search loop, providing a most suitable model for the target hardware platform. The results on different hardware platforms and datasets show that HAT searched models have better accuracy-efficiency trade-offs.