6 citations · 8 across the 13 of their papers we have counts for
3 papers · 1 filter
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…
Cut Tracing with E-Graphs for Boolean FHE Circuit Synthesis
Julien de Castelnau, Mingfei Yu, Giovanni De Micheli
Fully Homomorphic Encryption (FHE) is a promising privacy-preserving technology enabling secure computation over encrypted data. A major limitation of current FHE schemes is their…
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…