The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
PipeLayer: A Pipelined ReRAM-Based Accelerator for Deep Learning
Abstract
Convolution neural networks (CNNs) are the heart of deep learning applications. Recent works PRIME [1] and ISAAC [2] demonstrated the promise of using resistive random access memory (ReRAM) to perform neural computations in memory. We found that training cannot be efficiently supported with the current schemes. First, they do not consider weight update and complex data dependency in training procedure. Second, ISAAC attempts to increase system throughput with a very deep pipeline. It is only beneficial when a large number of consecutive images can be fed into the architecture. In training, the notion of batch (e.g. 64) limits the number of images can be processed consecutively, because the images in the next batch need to be processed based on the updated weights. Third, the deep pipeline in ISAAC is vulnerable to pipeline bubbles and execution stall. In this paper, we present PipeLayer, a ReRAM-based PIM accelerator for CNNs that support both training and testing. We analyze data dependency and weight update in training algorithms and propose efficient pipeline to exploit inter-layer parallelism. To exploit intra-layer parallelism, we propose highly parallel design based on the notion of parallelism granularity and weight replication. With these design choices, PipeLayer enables the highly pipelined execution of both training and testing, without introducing the potential stalls in previous work. The experiment results show that, PipeLayer achieves the speedups of 42.45x compared with GPU platform on average. The average energy saving of PipeLayer compared with GPU implementation is 7.17x.
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Cites
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Cited by
Memory devices and applications for in-memory computing
Sebastian, A., Le Gallo, M., Khaddam-Aljameh, R., & Eleftheriou, E. (2020) · Nature Nanotechnology · doi:10.1038/s41565-020-0655-z · Cited by 2,289
Neuro-inspired computing chips
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Bit Fusion: Bit-Level Dynamically Composable Architecture for Accelerating Deep Neural Network
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Hardware implementation of memristor-based artificial neural networks
Aguirre, F., Sebastian, A., Le Gallo, M., Song, W., Wang, T., Yang, J. J., Lu, W., Chang, M.-F., Ielmini, D., Yang, Y., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-45670-9 · Cited by 564
“Alternatives to overcome this lim-itationhavebeenproposed,asforinstancethesimulatordevelopedby Song et al. 246 to evaluate their PipeLayer architecture, which considers highly parallel designs based on the notion of parallelism granularity and weight replication.”
Long short-term memory networks in memristor crossbar arrays
Li, C., Wang, Z., Rao, M., Belkin, D., Song, W., Jiang, H., Yan, P., Li, Y., Lin, P., Hu, M., et al. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-018-0001-4 · Cited by 429
Metadata
- Authors
- Song, L., Qian, X., Li, H., & Chen, Y.
- Venue
- 2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)
- Publisher
- IEEE
- Published
- 2017-02
- Pages
- 541-552
- ISSN
- 1530-0897 (ISSN-L)
- OpenAlex
- W2613989746
- Cited by
- 833
- References
- 64
- FWCI
- 113.24
- Type
- conference paper
- Language
- en
- Open access
- closed
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