A5022639653- Advanced Memory and Neural Computing
- EEG and Brain-Computer Interfaces
- CCD and CMOS Imaging Sensors
- Advanced Neural Network Applications
- Computer Graphics and Visualization Techniques
- Advanced Numerical Analysis Techniques
- 3D Shape Modeling and Analysis
- Human Pose and Action Recognition
- Epilepsy research and treatment
- Ferroelectric and Negative Capacitance Devices
- Functional Brain Connectivity Studies
- Advanced Image and Video Retrieval Techniques
Peking University
2021-2025
The monitoring of epilepsy patients in non-hospital environment is highly desirable, where ultra-low power wearable seizure detection devices are essential such a system. state-of-the-art epileptic algorithms targeting either rely on manual feature extractions, which can be biased due to the experience experts, or deep neural networks, suffer from high computation complexity. In this paper, we propose lightweight learning model, LightSeizureNet (LSN), for real-time based raw EEG data...
Three-dimensional (3-D) understanding or inference has received increasing attention, where 3-D convolutional neural networks (3D-CNNs) have demonstrated superior performance compared to 2D-CNNs, since 3D-CNNs learn features from all three dimensions. However, suffer intensive computation and data movement. In this article, Sagitta, an energy-efficient low-latency on-chip 3D-CNN accelerator, is proposed for edge devices. Locality small differential value dropout are leveraged increase the...
An energy-efficient convolutional neural network (CNN) accelerator is proposed for low-power inference on edge devices. adaptive zero skipping technique to dynamically skip the zeros in either activations or weights, depending which has higher sparsity. The characteristic of non-zero data aggregation explored enhance effectiveness performance boosting. To mitigate load imbalance issue after skipping, a sparsity-driven flow and low-complexity dynamic task allocation are employed different...
Three-dimensional convolutional neural network (3D-CNN) has demonstrated outstanding classification performance in video recognition compared to two-dimensional CNN (2D-CNN), since 3D-CNN not only learns the spatial features of each frame, but also temporal across all frames. However, suffers from intensive computation and data movement. To solve these issues, an energy-efficient low-latency accelerator is proposed. Temporal locality small differential value dropout are used increase...
Three-dimensional (3D) point cloud has been employed in a wide range of applications recently. As powerful weapon for analysis, point-based neural networks (PNNs) have demonstrated superior performance with less computation complexity and parameters, compared to sparse 3D convolution-based graph-based convolutional networks. However, PNNs still suffer from high computational redundancy, large off-chip memory access, low parallelism hardware implementation, thereby hindering the on edge...