5 citations · 6 across the 2 of their papers we have counts for
3 papers
cs.AR2026
StateTune: Transforming LLM-Assisted EDA Flow Tuning into a Stateful, Closed-Loop Process
Kunlong Li, Shangshang Yao, Su Zheng +1
EDA flow parameter tuning is critical for quality-of-results~(QoR), yet the parameter space is large, tightly coupled, and full evaluations are prohibitively expensive. Prior LLM-a…
cs.AR2022★ 5 cited
Low Error-Rate Approximate Multiplier Design for DNNs with Hardware-Driven Co-Optimization
Yao Lu, Jide Zhang, Su Zheng +2
In this paper, two approximate 3*3 multipliers are proposed and the synthesis results of the ASAP-7nm process library justify that they can reduce the area by 31.38% and 36.17%, an…
cs.AR2022★ 1 cited
HEAM: High-Efficiency Approximate Multiplier Optimization for Deep Neural Networks
Su Zheng, Zhen Li, Yao Lu +3
We propose an optimization method for the automatic design of approximate multipliers, which minimizes the average error according to the operand distributions. Our multiplier achi…