A5046094842- Advanced Memory and Neural Computing
- Advanced Neural Network Applications
- Ferroelectric and Negative Capacitance Devices
- VLSI and Analog Circuit Testing
- Low-power high-performance VLSI design
- CCD and CMOS Imaging Sensors
- Neural Networks and Applications
Southern University of Science and Technology
2022-2024
Computing-In-memory (CIM) accelerators have the characteristics of storage and computing integration, which has potential to break through limit Moore's law bottleneck Von-Neumann architecture for convolutional neural networks (CNN) implementation improvement. However, performance CIM is still limited by conventional CNN architectures inefficient readouts. To increase energy-efficient performance, an optimized model required a low-power column parallel readout necessary edge-computing...
Multi-bit-width neural network enlightens a promising method for high performance yet energy efficient edge computing due to its balance between software algorithm accuracy and hardware efficiency. To date, FPGA has been one of the core platforms deploying various networks. However, it is still difficult fully make use dedicated digital signal processing (DSP) blocks in accelerating multi-bit-width network. In this work, we develop state-of-the-art convolutional accelerator with novel...
Computing-In-memory (CIM) accelerators have the characteristics of storage and computing integration, which has potential to break through limit Moore's law bottleneck Von-Neumann architecture. However, performance CIM is still limited by conventional CNN architectures inefficient readouts. To increase energy-efficient performance, optimized model required low-power fully parallel readout necessary for edge-computing hardware. In this work, an ReRAM-based accelerator designed. Mixed-bit...