Search Results for author:
Found 8 papers, 2 papers with code
From Isolated Islands to Pangea: Unifying Semantic Space for Human Action Understanding
By aligning the classes of previous datasets to our semantic space, we gather (image/video/skeleton/MoCap) datasets into a unified database in a unified label system, i. e., bridging "isolated islands" into a "Pangea".
DeepOHeat: Operator Learning-based Ultra-fast Thermal Simulation in 3D-IC Design
In this paper, for the first time, we propose DeepOHeat, a physics-aware operator learning framework to predict the temperature field of a family of heat equations with multiple parametric or non-parametric design configurations.
Systolic-CNN: An OpenCL-defined Scalable Run-time-flexible FPGA Accelerator Architecture for Accelerating Convolutional Neural Network Inference in Cloud/Edge Computing
Systolic-CNN is also run-time-flexible in the context of multi-tenancy cloud/edge computing, which can be time-shared to accelerate a variety of CNN models at run time without the need of recompiling the FPGA kernel hardware nor reprogramming the FPGA.
Light-Weight RetinaNet for Object Detection
On the contrary, very few studies discuss the computation and accuracy trade-off scheme for mAP-30-tier detection networks.
Build a Compact Binary Neural Network through Bit-level Sensitivity and Data Pruning
Emerging solutions are adopted for the neural network compression, e. g., bina-ry/ternary weight network, pruned network and quantized network.
A GPU-Outperforming FPGA Accelerator Architecture for Binary Convolutional Neural Networks
For processing static data in large batch sizes, the proposed solution is on a par with a Titan X GPU in terms of throughput while delivering 9. 5x higher energy efficiency.
A Data-Driven Compressive Sensing Framework Tailored For Energy-Efficient Wearable Sensing
Compressive sensing (CS) is a promising technology for realizing energy-efficient wireless sensors for long-term health monitoring.
A Binary Convolutional Encoder-decoder Network for Real-time Natural Scene Text Processing
In this paper, we develop a binary convolutional encoder-decoder network (B-CEDNet) for natural scene text processing (NSTP).
