# GraphR: Accelerating Graph Processing Using ReRAM

arXiv: https://arxiv.org/abs/1708.06248 · HTML: https://mlsanity.com/paper/1708.06248/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2017.

## Built on by (most cited first, 19 of 19)

- [Accelerating Large Scale Real-Time GNN Inference using Channel Pruning](https://mlsanity.com/paper/2105.04528/) (2021)
- [A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware](https://mlsanity.com/paper/2306.14052/) (2023)
- [Sparse Attention Acceleration with Synergistic In-Memory Pruning and On-Chip Recomputation](https://mlsanity.com/paper/2209.00606/) (2022)
- [On the Accuracy of Analog Neural Network Inference Accelerators](https://mlsanity.com/paper/2109.01262/) (2021)
- [Accelerating Neural Network Inference with Processing-in-DRAM: From the Edge to the Cloud](https://mlsanity.com/paper/2209.08938/) (2022)
- [FARe: Fault-Aware GNN Training on ReRAM-based PIM Accelerators](https://mlsanity.com/paper/2401.10522/) (2024)
- [Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases](https://mlsanity.com/paper/2205.14664/) (2022)
- [Harmonia: Enhancing Data Placement and Migration in Hybrid Storage Systems via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2503.20507/) (2025)
- [End-to-End Transformer Acceleration Through Processing-in-Memory Architectures](https://mlsanity.com/paper/2601.14260/) (2025)
- [Pyxis: An Open-Source Performance Dataset of Sparse Accelerators](https://mlsanity.com/paper/2110.04280/) (2021)
- [AutoGMap: Learning to Map Large-scale Sparse Graphs on Memristive Crossbars](https://mlsanity.com/paper/2111.07684/) (2021)
- [COIN: Communication-Aware In-Memory Acceleration for Graph Convolutional Networks](https://mlsanity.com/paper/2205.07311/) (2022)
- [NEON: Enabling Efficient Support for Nonlinear Operations in Resistive RAM-based Neural Network Accelerators](https://mlsanity.com/paper/2211.05730/) (2022)
- [Accelerating Generic Graph Neural Networks via Architecture, Compiler, Partition Method Co-Design](https://mlsanity.com/paper/2308.08174/) (2023)
- [Pruning random resistive memory for optimizing analogue AI](https://mlsanity.com/paper/2311.07164/) (2023)
- [A Two Level Neural Approach Combining Off-Chip Prediction with Adaptive Prefetch Filtering](https://mlsanity.com/paper/2403.15181/) (2024)
- [DARTH-PUM: A Hybrid Processing-Using-Memory Architecture](https://mlsanity.com/paper/2602.16075/) (2026)
- [TokenStack: A Heterogeneous HBM-PIM Architecture and Runtime for Efficient LLM Inference](https://mlsanity.com/paper/2605.05639/) (2026)
- [ThRIve: Thermally Robust CNN Inference via Low-Rank Adaptation in Heterogeneous PIM Architectures](https://mlsanity.com/paper/2607.17091/) (2026)

## Abstract (arXiv)

This paper presents GRAPHR, the first ReRAM-based graph processing accelerator. GRAPHR follows the principle of near-data processing and explores the opportunity of performing massive parallel analog operations with low hardware and energy cost. The analog computation is suit- able for graph processing because: 1) The algorithms are iterative and could inherently tolerate the imprecision; 2) Both probability calculation (e.g., PageRank and Collaborative Filtering) and typical graph algorithms involving integers (e.g., BFS/SSSP) are resilient to errors. The key insight of GRAPHR is that if a vertex program of a graph algorithm can be expressed in sparse matrix vector multiplication (SpMV), it can be efficiently performed by ReRAM crossbar. We show that this assumption is generally true for a large set of graph algorithms. GRAPHR is a novel accelerator architecture consisting of two components: memory ReRAM and graph engine (GE). The core graph computations are performed in sparse matrix format in GEs (ReRAM crossbars). The vector/matrix-based graph computation is not new, but ReRAM offers the unique opportunity to realize the massive parallelism with unprecedented energy efficiency and low hardware cost. With small subgraphs processed by GEs, the gain of performing parallel operations overshadows the wastes due to sparsity. The experiment results show that GRAPHR achieves a 16.01x (up to 132.67x) speedup and a 33.82x energy saving on geometric mean compared to a CPU baseline system. Com- pared to GPU, GRAPHR achieves 1.69x to 2.19x speedup and consumes 4.77x to 8.91x less energy. GRAPHR gains a speedup of 1.16x to 4.12x, and is 3.67x to 10.96x more energy efficiency compared to PIM-based architecture.