TL-nvSRAM-CIM: Ultra-High-Density Three-Level ReRAM-Assisted Computing-in-nvSRAM with DC-Power Free Restore and Ternary MAC Operations

no code implementations • 6 Jul 2023 • Dengfeng Wang, Liukai Xu, Songyuan Liu, Zhi Li, Yiming Chen, Weifeng He, Xueqing Li, Yanan sun

Accommodating all the weights on-chip for large-scale NNs remains a great challenge for SRAM based computing-in-memory (SRAM-CIM) with limited on-chip capacity.

A 65nm 8b-Activation 8b-Weight SRAM-Based Charge-Domain Computing-in-Memory Macro Using A Fully-Parallel Analog Adder Network and A Single-ADC Interface

no code implementations • 23 Nov 2022 • Guodong Yin, Mufeng Zhou, Yiming Chen, Wenjun Tang, Zekun Yang, Mingyen Lee, Xirui Du, Jinshan Yue, Jiaxin Liu, Huazhong Yang, Yongpan Liu, Xueqing Li

Performing data-intensive tasks in the von Neumann architecture is challenging to achieve both high performance and power efficiency due to the memory wall bottleneck.

Quantization

UGRec: Modeling Directed and Undirected Relations for Recommendation

1 code implementation • 10 May 2021 • Xinxiao Zhao, Zhiyong Cheng, Lei Zhu, Jiecai Zheng, Xueqing Li

In particular, for a directed relation, we transform the head and tail entities into the corresponding relation space to model their relation; and for an undirected co-occurrence relation, we project head and tail entities into a unique hyperplane in the entity space to minimize their distance.

Attribute Collaborative Filtering +2

Enabling Lower-Power Charge-Domain Nonvolatile In-Memory Computing with Ferroelectric FETs

no code implementations • 2 Feb 2021 • Guodong Yin, Yi Cai, Juejian Wu, Zhengyang Duan, Zhenhua Zhu, Yongpan Liu, Yu Wang, Huazhong Yang, Xueqing Li

Compute-in-memory (CiM) is a promising approach to alleviating the memory wall problem for domain-specific applications.

Emerging Technologies