3 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.ET2026
Simulation-Guided Approximate Logic Synthesis Under the Maximum Error Constraint
Chang Meng, Weikang Qian, Giovanni De Micheli
Approximate computing is an effective computing paradigm for improving the energy efficiency of error-tolerant applications. Approximate logic synthesis (ALS) is an automatic proce…
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…