37 citations · 104 across the 11 of their papers we have counts for
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cs.CR2026
Think Locally Before Computing Jointly: Efficient Secure Multi-Party Computation via Maximizing Local Processing
Mingfei Yu, Chenglu Jin, Giovanni De Micheli
Secure multi-party computation (MPC) lets parties evaluate a function over private inputs without revealing them. In garbled-circuit (GC) protocols, prior optimization has largely…
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…