# AutoGMap: Learning to Map Large-scale Sparse Graphs on Memristive Crossbars

arXiv: https://arxiv.org/abs/2111.07684 · HTML: https://mlsanity.com/paper/2111.07684/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 63 references that are in the map.
- First posted on arXiv 15 Nov 2021.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Graph-based Semi-supervised Learning: A Comprehensive Review](https://mlsanity.com/paper/2102.13303/) (2021)
- [GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks](https://mlsanity.com/paper/2007.03179/) (2020)
- [GraphR: Accelerating Graph Processing Using ReRAM](https://mlsanity.com/paper/1708.06248/) (2017)
- [Neuro-memristive Circuits for Edge Computing: A review](https://mlsanity.com/paper/1807.00962/) (2018)
- [Fast Training of Sparse Graph Neural Networks on Dense Hardware](https://mlsanity.com/paper/1906.11786/) (2019)
- [Distributed Average Tracking for Double-integrator Multi-agent Systems with Reduced Requirement on Velocity Measurements](https://mlsanity.com/paper/1507.04780/) (2015)
- [TND-NAS: Towards Non-differentiable Objectives in Progressive Differentiable NAS Framework](https://mlsanity.com/paper/2111.03892/) (2021)

## Abstract (arXiv)

The sparse representation of graphs has shown great potential for accelerating the computation of graph applications (e.g., Social Networks, Knowledge Graphs) on traditional computing architectures (CPU, GPU, or TPU). But the exploration of large-scale sparse graph computing on processing-in-memory (PIM) platforms (typically with memristive crossbars) is still in its infancy. To implement the computation or storage of large-scale or batch graphs on memristive crossbars, a natural assumption is that a large-scale crossbar is demanded, but with low utilization. Some recent works question this assumption, to avoid the waste of storage and computational resource, the fixed-size or progressively scheduled ''block partition'' schemes are proposed. However, these methods are coarse-grained or static, and are not effectively sparsity-aware. This work proposes the dynamic sparsity-aware mapping scheme generating method that models the problem with a sequential decision-making model, and optimizes it by reinforcement learning (RL) algorithm (REINFORCE). Our generating model (LSTM, combined with the dynamic-fill scheme) generates remarkable mapping performance on a small-scale graph/matrix data (complete mapping costs 43% area of the original matrix) and two large-scale matrix data (costing 22.5% area on qh882 and 17.1% area on qh1484). Our method may be extended to sparse graph computing on other PIM architectures, not limited to the memristive device-based platforms.