Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Yongpan Liu"'
Publikováno v:
Proceedings of the 28th Asia and South Pacific Design Automation Conference.
Autor:
Yiming Chen, Guodong Yin, Mingyen Lee, Wenjun Tang, Zekun Yang, Yongpan Liu, Huazhong Yang, Xueqing Li
Publikováno v:
Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design.
Autor:
Yiming Chen, Guodong Yin, Zhanhong Tan, Mingyen Lee, Zekun Yang, Yongpan Liu, Huazhong Yang, Kaisheng Ma, Xueqing Li
Publikováno v:
Proceedings of the 59th ACM/IEEE Design Automation Conference.
Computing-in-memory (CiM) is a promising technique to achieve high energy efficiency in data-intensive matrix-vector multiplication (MVM) by relieving the memory bottleneck. Unfortunately, due to the limited SRAM capacity, existing SRAM-based CiM nee
Publikováno v:
ASP-DAC
Nowadays, deep neural network (DNN) has played an important role in machine learning. Non-volatile computing-in-memory (nvCIM) for DNN has become a new architecture to optimize hardware performance and energy efficiency. However, the existing nvCIM a
Publikováno v:
ASP-DAC
Block-circulant based compression is a popular technique to accelerate neural network inference. Though storage and computing costs can be reduced by transforming weights into block-circulant matrices, this method incurs uneven data distribution in t
Autor:
Mengying Zhao, Keni Qiu, Zhenge Jia, Vijaykrishnan Narayanan, Kaisheng Ma, Xueqing Li, Jingtong Hu, Yongpan Liu, Chun Jason Xue
Publikováno v:
ACM Great Lakes Symposium on VLSI
There is growing interest in deploying energy harvesting processors and accelerators in Internet of Things (IoT). Energy harvesting harnesses the energy scavenged from the environment to power a system. Although it has many advantages over battery-op
Publikováno v:
ISLPED
Activation I/O traffic is a critical bottleneck of video neural network processor. Recent works adopted an inter-frame difference method to reduce activation size. However, current methods can't fully adapt to the various precision and sparsity in di
Autor:
Xueqing Li, Vijaykrishnan Narayanan, Yu Wang, Mingyuan Ma, Mingyen Lee, Deliang Fan, Juejian Wu, Yongpan Liu, Tang Wenjun, Bowen Xue, Huazhong Yang
Publikováno v:
ISLPED
Compute-in-memory (CiM) is a promising method for mitigating the memory wall problem in data-intensive applications. The proposed bitwise logic-in-memory (BLiM) is targeted at data intensive applications, such as database, data encryption. This work
Publikováno v:
DAC
Making embedded memory symmetric provides the capability of memory access in both rows and columns, which brings new opportunities of significant energy and time savings if only a portion of data in the words need to be accessed. This work investigat
Autor:
Shuangchen Li, Fang Su, Zhibo Wang, Zhe Yuan, Huazhong Yang, Wenyu Sun, Yongpan Liu, Xueqing Li, Jinshan Yue
Publikováno v:
ASP-DAC
ReRAM-based processing-in-memory (PIM) architecture is a promising solution for deep neural networks (NN), due to its high energy efficiency and small footprint. However, traditional PIM architecture has to use a separate crossbar array to store eith