30 citations · 74 across the 13 of their papers we have counts for
4 papers · 1 filter
Unicorn-CIM: Uncovering the Vulnerability and Improving the Resilience of High-Precision Compute-in-Memory
Qiufeng Li, Yiwen Liang, Weidong Cao
Compute-in-memory (CIM) architecture has been widely explored to address the von Neumann bottleneck in accelerating deep neural networks (DNNs). However, its reliability remains la…
A Hybrid-Domain Floating-Point Compute-in-Memory Architecture for Efficient Acceleration of High-Precision Deep Neural Networks
Zhiqiang Yi, Yiwen Liang, Weidong Cao
Compute-in-memory (CIM) has shown significant potential in efficiently accelerating deep neural networks (DNNs) at the edge, particularly in speeding up quantized models for infere…
RoSE-Opt: Robust and Efficient Analog Circuit Parameter Optimization with Knowledge-infused Reinforcement Learning
Weidong Cao, Jian Gao, Tianrui Ma +3
This paper proposes a learning framework, RoSE-Opt, to achieve robust and efficient analog circuit parameter optimization. RoSE-Opt has two important features. First, it incorporat…
Neural-PIM: Efficient Processing-In-Memory with Neural Approximation of Peripherals
Weidong Cao, Yilong Zhao, Adith Boloor +3
Processing-in-memory (PIM) architectures have demonstrated great potential in accelerating numerous deep learning tasks. Particularly, resistive random-access memory (RRAM) devices…