4 papers
Accuracy-Configurable Floating-Point Multiplier Design for SRAM-Based Compute-in-Memory
Yiqi Zhou, Junhao Lu, Jiale Yu +5
Digital Compute-in-Memory (DCiM) reduces data movement and has become a promising solution for energy-efficient edge AI. However, most existing DCiM frameworks still primarily targ…
OpenACMv2: An Accuracy-Constrained Co-Optimization Framework for Approximate DCiM
Yiqi Zhou, Yue Yuan, Yikai Wang +8
Digital Compute-in-Memory (DCiM) accelerates neural networks by reducing data movement. Approximate DCiM can further improve power-performance-area (PPA), but demands accuracy-cons…
R2G: A Multi-View Circuit Graph Benchmark Suite from RTL to GDSII
Zewei Zhou, Jiajun Zou, Jiajia Zhang +8
Graph neural networks (GNNs) are increasingly applied to physical design tasks such as congestion prediction and wirelength estimation, yet progress is hindered by inconsistent cir…
OpenACM: An Open-Source SRAM-Based Approximate CiM Compiler
Yiqi Zhou, JunHao Ma, Xingyang Li +10
The rise of data-intensive AI workloads has exacerbated the ``memory wall'' bottleneck. Digital Compute-in-Memory (DCiM) using SRAM offers a scalable solution, but its vast design…