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Daedalus A Physics-Inspired Mixed-Signal Optimization Engine With Dynamic Contin
Daedalus：一种受物理启发的混合信号优化引擎，用于加速解决NP完全组合优化问题。
28nm CMOS, 50变量问题解决时间1.6µs（20变量）和31.7µs（50变量）, 解决能耗7.8nJ（20变量）和268.9nJ（50变量）
物理启发计算混合信号优化NP完全问题3-SAT求解弛豫振荡器
▸引入Daedalus，一种基于振荡器的大规模并行直接3-SAT引擎
▸采用连续时间动力学系统，具有三体自旋相互作用和非线性自旋耦合
▸基于弛豫振荡器（RXO）的自旋与动态连续时间注入（DaCTI）技术
Abstract
Nondeterministic polynomial-time complete (NP-
complete) combinatorial optimization problems (COPs), such as
Boolean satisfiability (SAT), are intractable on classical com-
puting architectures, often resulting in exponential scaling of
solution time and energy as the problem size increases. Inspired
by natural physical interactions, physics-inspired computers
harness the continuous-time (CT) dynamics of coupled spins,
massive parallelism, and analog computation to accelerate solving
COPs. This w