4 papers · 1 filter
CIMinus: Empowering Sparse DNN Workloads Modeling and Exploration on SRAM-based CIM Architectures
Yingjie Qi, Jianlei Yang, Rubing Yang +5
Compute-in-memory (CIM) has emerged as a pivotal direction for accelerating workloads in the field of machine learning, such as Deep Neural Networks (DNNs). However, the effective…
MIREDO: MIP-Driven Resource-Efficient Dataflow Optimization for Computing-in-Memory Accelerator
Xiaolin He, Cenlin Duan, Yingjie Qi +2
Computing-in-Memory (CIM) architectures have emerged as a promising solution for accelerating Deep Neural Networks (DNNs) by mitigating data movement bottlenecks. However, realizin…
Efficient SRAM-PIM Co-design by Joint Exploration of Value-Level and Bit-Level Sparsity
Cenlin Duan, Jianlei Yang, Yikun Wang +7
Processing-in-memory (PIM) is a transformative architectural paradigm designed to overcome the Von Neumann bottleneck. Among PIM architectures, digital SRAM-PIM emerges as a promis…
CIMFlow: An Integrated Framework for Systematic Design and Evaluation of Digital CIM Architectures
Yingjie Qi, Jianlei Yang, Yiou Wang +6
Digital Compute-in-Memory (CIM) architectures have shown great promise in Deep Neural Network (DNN) acceleration by effectively addressing the "memory wall" bottleneck. However, th…