SAGA: Synthesis Augmentation with Genetic Algorithms for InMemory Sequence Optimization
Abstract
The vonNeumann 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 inmemory 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 inmemory computation. SAGA models the execution sequences as a topological sorting problem which makes the optimization wellsuited 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 memoryfootprint needed for evaluating each of these circuits is decreased by up to 52% from existing, greedyalgorithmbased optimization solutions. Over the 10 benchmark circuits evaluated, these modifications lead to an overall improvement in the efficiency of inmemory circuit evaluation of 128% in the best case and 27.5% on average.
 Publication:

arXiv eprints
 Pub Date:
 June 2024
 DOI:
 10.48550/arXiv.2406.09677
 arXiv:
 arXiv:2406.09677
 Bibcode:
 2024arXiv240609677R
 Keywords:

 Computer Science  Neural and Evolutionary Computing;
 Computer Science  Hardware Architecture
 EPrint:
 6 pages, 2 Figures, 3 Tables