42 citations · 92 across the 5 of their papers we have counts for
5 papers
STAR: An Efficient Softmax Engine for Attention Model with RRAM Crossbar
Yifeng Zhai, Bing Li, Bonan Yan +1
RRAM crossbars have been studied to construct in-memory accelerators for neural network applications due to their in-situ computing capability. However, prior RRAM-based accelerato…
NAND-SPIN-Based Processing-in-MRAM Architecture for Convolutional Neural Network Acceleration
Yinglin Zhao, Jianlei Yang, Bing Li +7
The performance and efficiency of running large-scale datasets on traditional computing systems exhibit critical bottlenecks due to the existing "power wall" and "memory wall" prob…
RED: A ReRAM-based Deconvolution Accelerator
Zichen Fan, Ziru Li, Bing Li +3
Deconvolution has been widespread in neural networks. For example, it is essential for performing unsupervised learning in generative adversarial networks or constructing fully con…
An Overview of In-memory Processing with Emerging Non-volatile Memory for Data-intensive Applications
Bing Li, Bonan Yan, Hai +1
The conventional von Neumann architecture has been revealed as a major performance and energy bottleneck for rising data-intensive applications. %, due to the intensive data moveme…
Thread Batching for High-performance Energy-efficient GPU Memory Design
Bing Li, Mengjie Mao, Xiaoxiao Liu +6
Massive multi-threading in GPU imposes tremendous pressure on memory subsystems. Due to rapid growth in thread-level parallelism of GPU and slowly improved peak memory bandwidth, t…