Publications

GACT is an activation compression training (ACT) framework to support a broad range of machine learning tasks for generic neural network architectures with limited domain knowledge. By analyzing a linearized version of ACT’s approximate gradient, we prove the convergence of GACT without prior knowledge on operator type or model architecture.

ActNN is a PyTorch library for memory-efficient training. It reduces the training memory footprint by compressing the saved activations. ActNN is implemented as a collection of memory-saving layers. These layers have an identical interface to their PyTorch counterparts.

Co-design of a deformable convolution operation on FPGA with hardware-friendly modifications, showing up to 9.76× hardware speedup. Development of an efficient DNN model for object detection with co-designed input-adaptive deformable convolution that achieves 67.1 AP50 on Pascal VOC with 2.9 MB parameters. The model is 20.9× smaller but 10% more accurate than the Tiny-YOLO. Implementation of an FPGA accelerator to support the target neural network design that runs at 26 frames per second on Pascal VOC with 61.7 AP50.