论文标题
来自输入输出示例的神经组合逻辑电路合成
Neural Combinatorial Logic Circuit Synthesis from Input-Output Examples
论文作者
论文摘要
我们提出了一种新颖的，完全可以解释的神经方法，用于从输入输出示例中合成组合逻辑回路。我们方法的携带优势在于，它很容易扩展到归纳场景，其中一组示例不完整，但仍表示所需的行为。只要可以以可不同的方式制定我们的方法，我们的方法几乎可以用于原子的几乎任意选择 - 从逻辑门到FPGA块，并且可以始终取得良好的结果，以综合尺寸增加的实用电路。特别是，我们成功地学习了许多算术，位和信号路由操作，甚至在归纳场景中推广到正确的行为。我们的方法是通过可解释的神经方法攻击离散的逻辑合成问题，暗示了对合成和与推理相关的任务的更广泛的希望。
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.
