We propose a novel, fully explainable neural approach to synthesis of
combinatorial logic circuits from input-output examples. The carrying advantage
of our method is that it readily extends to inductive scenarios, where the set
of examples is incomplete but still indicative of the desired behaviour. Our
method can be employed for a virtually arbitrary choice of atoms - from logic
gates to FPGA blocks - as long as they can be formulated in a differentiable
fashion, and consistently yields good results for synthesis of practical
circuits of increasing size. In particular, we succeed in learning a number of
arithmetic, bitwise, and signal-routing operations, and even generalise towards
the correct behaviour in inductive scenarios. Our method, attacking a discrete
logical synthesis problem with an explainable neural approach, hints at a wider
promise for synthesis and reasoning-related tasks.

本研究提出了一种新颖的可完全解释的神经方法来合成组合逻辑电路，其特点是可以扩展到归纳情形并成功地学习了多种算术、位运算和信号路由操作，并且对于合成逐渐增大的实际电路始终产生良好的结果。