2 papers
cs.LG2026
TRAM: Training Approximate Multiplier Structures for Low-Power AI Accelerators
Chang Meng, Hanyu Wang, Yuyang Ye +3
Reducing power consumption in AI accelerators is increasingly important. Approximate computing can reduce power consumption while keeping the accuracy loss small. Since multipliers…
cs.LG2025
Gradient Estimation Methods of Approximate Multipliers for High-Accuracy Retraining of Deep Learning Models
Chang Meng, Wayne Burleson, Giovanni De Micheli
Approximate multipliers (AppMults) are widely used in deep learning accelerators to reduce their area, delay, and power consumption. However, AppMults introduce arithmetic errors i…