SAGA：利用遗传算法进行综合增强以实现存内序列优化
SAGA: Synthesis Augmentation with Genetic Algorithms for In-Memory Sequence Optimization
AI总结：
针对 MAGIC 存内计算的操作排序难题，本文提出 SAGA，将执行序列建模为拓扑排序并用遗传算法优化，显著降低内存占用并提升电路评估效率。
摘要：
冯·诺依曼架构存在一个瓶颈，限制了可供计算使用的数据获取速度。为解决这一问题，人们正在开发新型计算范式。其中一种范式称为存内计算，它将计算与数据存储交错安排在相同电路内。MAGIC（Memristor Aided Logic，忆阻器辅助逻辑）是一种使用存储电路的方法，这些电路通过向存储器执行写操作来物理地完成计算。对这些操作进行排序是一个计算上困难的问题，并且与使用基于 MAGIC 的存内计算所得解决方案的成本直接相关。SAGA 将执行序列建模为拓扑排序问题，使该优化非常适合采用遗传算法。随后，我们详细说明了这些遗传算法的构造与实现，并在若干开路电路实现上对其进行评估。与现有基于贪心算法的优化方案相比，评估每个电路所需的内存占用最多降低 52%。在所评估的 10 个基准电路上，这些改进使存内电路评估的效率总体提升，最佳情况下达到 128%，平均为 27.5%。
英文摘要：
The von-Neumann architecture has a bottleneck which limits the speed at which data can be made available for computation. To combat this problem, novel paradigms for computing are being developed. One such paradigm, known as in-memory computing, interleaves computation with the storage of data within the same circuits. MAGIC, or Memristor Aided Logic, is an approach which uses memory circuits which physically perform computation through write operations to memory. Sequencing these operations is a computationally difficult problem which is directly correlated with the cost of solutions using MAGIC based in-memory computation. SAGA models the execution sequences as a topological sorting problem which makes the optimization well-suited for genetic algorithms. We then detail the formation and implementation of these genetic algorithms and evaluate them over a number of open circuit implementations. The memory-footprint needed for evaluating each of these circuits is decreased by up to 52% from existing, greedy-algorithm-based optimization solutions. Over the 10 benchmark circuits evaluated, these modifications lead to an overall improvement in the efficiency of in-memory circuit evaluation of 128% in the best case and 27.5% on average.
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