---
# System prepended metadata

title: '[Index] DLRM research progress'
tags: [research-DLRM]

---

# [Index] DLRM research progress
###### tags: `research-DLRM`

## Progress 

### 2022/12/1-12/15 
- 
- Graph analytics accelerator for FPGAs
  - T. Ye, S. R. Kuppannagari, C. A. F. De Rose, S. Wijeratne, R. Kannan, and V. K. Prasanna, **“Estimating the Impact of Communication Schemes for Distributed Graph Processing,”** pp. 49–56, 2022, doi: 10.1109/ispdc55340.2022.00016.
  - R. Garg et al., **“A Taxonomy for Classification and Comparison of Dataflows for GNN Accelerators,”** 36th IEEE Int. Parallel Distrib. Process. Symp. (IPDPS 2022) CCF A, no. March, 2021, [Online]. Available: http://arxiv.org/abs/2103.07977
- GNN accelerator for FPGAs
  - R. Garg et al., “Understanding the Design-Space of Sparse/Dense Multiphase GNN dataflows on Spatial Accelerators,” Proc. - 2022 IEEE 36th Int. Parallel Distrib. Process. Symp. IPDPS 2022, pp. 571–582, 2022, doi: 10.1109/IPDPS53621.2022.00062.
  - R. Sarkar, S. Abi-Karam, Y. He, L. Sathidevi, and C. Hao, **“FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inference,”** no. 1, pp. 1–13, 2022, [Online]. Available: http://arxiv.org/abs/2204.13103
  - S. Abi-Karam, Y. He, R. Sarkar, L. Sathidevi, Z. Qiao, and C. Hao, **GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration,** vol. 1, no. 1. Association for Computing Machinery, 2022. [Online]. Available: http://arxiv.org/abs/2201.08475
  - C. Ogbogu et al., **“Accelerating Large-Scale Graph Neural Network Training on Crossbar Diet,”** IEEE Trans. Comput. Des. Integr. Circuits Syst., vol. 41, no. 11, pp. 3626–3637, 2022, doi: 10.1109/TCAD.2022.3197342.
  - B. Zhang, H. Zeng, and V. Prasanna, **“Low-latency Mini-batch GNN Inference on CPU-FPGA Heterogeneous Platform,”** vol. 1, no. 1. Association for Computing Machinery, 2022. [Online]. Available: http://arxiv.org/abs/2206.08536
  - B. Zhang, R. Kannan, and V. Prasanna, **“BoostGCN: A Framework for Optimizing GCN Inference on FPGA,”** Proc. - 29th IEEE Int. Symp. Field-Programmable Cust. Comput. Mach. FCCM 2021, pp. 29–39, 2021, doi: 10.1109/FCCM51124.2021.00012.
  - H. Zeng and V. Prasanna, **“GraphACT: Accelerating GCN training on CPU-FPGA heterogeneous platforms,”** FPGA 2020 - 2020 ACM/SIGDA Int. Symp. Field-Programmable Gate Arrays, pp. 255–265, 2020, doi: 10.1145/3373087.3375312.
  - Y. C. Lin, B. Zhang, V. Prasanna, and Y. C. Lin, **“HP-GNN: Generating High Throughput GNN Training Implementation on CPU-FPGA Heterogeneous Platform,”** vol. 1, no. 1. Association for Computing Machinery, 2022. doi: 10.1145/3490422.3502359.
  - A. I. Arka, B. K. Joardar, J. R. Doppa, P. P. Pande, and K. Chakrabarty, **“DARe: DropLayer-Aware Manycore ReRAM architecture for Training Graph Neural Networks,”** IEEE/ACM Int. Conf. Comput. Des. Dig. Tech. Pap. ICCAD, vol. 2021-Novem, 2021, doi: 10.1109/ICCAD51958.2021.9643511.

- Transformer accelerators

- GNN for EDAs
  - X. Gao, C. Deng, M. Liu, Z. Zhang, D. Z. Pan, and Y. Lin, **“Layout Symmetry Annotation for Analog Circuits with Graph Neural Networks,”** Proc. Asia South Pacific Des. Autom. Conf. ASP-DAC, pp. 152–157, 2021, doi: 10.1145/3394885.3431545.
  - M. Liu, W. J. Turner, G. F. Kokai, B. Khailany, D. Z. Pan, and H. Ren, **“Parasitic-Aware Analog Circuit Sizing with Graph Neural Networks and Bayesian Optimization,”** Proc. -Design, Autom. Test Eur. DATE, vol. 2021-Febru, pp. 1372–1377, 2021, doi: 10.23919/DATE51398.2021.9474253.
  - Z. Guo, M. Liu, J. Gu, S. Zhang, D. Z. Pan, and Y. Lin, **“A timing engine inspired graph neural network model for pre-routing slack prediction,”** Proc. - Des. Autom. Conf., pp. 1207–1212, 2022, doi: 10.1145/3489517.3530597.
  - W. Li, R. Li, Y. Ma, S. O. Chan, D. Pan, and B. Yu, **“Rethinking Graph Neural Networks for the Graph Coloring Problem,”** no. 1, pp. 1–7, 2022, [Online]. Available: http://arxiv.org/abs/2208.06975
  - 
### Week 5 (2022/10/3-10/7)
- Figure out how AXPY operation is used in pytorch to computed EmbeddingBag operation
### Week 4 (2022/9/26-9/30)
- Got COVID-19 very sick, no progress
### Week 3 (2022/9/19-9/23)
- Figure out the function call graph of EmbeddingBag
### Week 2 (2022/9/12-9/16)
- Decoding pytorch implementation of EmbeddingBag operation
### Week 1 (2022/9/5-9/9)
- Prepare for ICCAD 2022 poster & oral presentation

## Hyper Links
- [Pytorch experimental env setup](https://hackmd.io/_0pdeCrGTbuOirOECZYRlw)
- [How Embedding & EmbeddingBag are used in NLP](https://hackmd.io/10r1SgYtRaWnijbNbrZNTQ)
- [Tracing EmbeddingBag module source code in pytorch](https://hackmd.io/4KqAif1zQ3KZkQ_pJ50KNg)
- [Understanding pytorch Tensor librar(ies)](https://hackmd.io/@WeiCheng14159/r1OQ0wc7o)