PipeBERT: High-throughput BERT Inference for ARM Big.LITTLE Multi-core Processors
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
PipeBERT's binary search algorithm completes much faster than naive binary search and brute-force search.
“Their binary search algorithm completes much faster than naive binary search and brute-force search.”
From A Survey of Techniques for Optimizing Transformer Inference (2023) · cites this paper as [253]
PipeBERT is a pipelining technique to accelerate BERT models on big.LITTLE processors.
“PipeBERT [253] is a pipelining technique to accelerate BERT models on big.LITTLE processors.”
From A Survey of Techniques for Optimizing Transformer Inference (2023) · cites this paper as [253]
On the HiKey970 system, PipeBERT's technique achieves much higher throughput than executing on four big cores and a much lower energy-delay product than the best single-cluster execution.
“On the HiKey970 system, their technique achieves much higher throughput than executing on four big cores and a much lower 'energy-delay product' than the best single-cluster execution.”
From A Survey of Techniques for Optimizing Transformer Inference (2023) · cites this paper as [253]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices
Sun, Z., Yu, H., Song, X., Liu, R., Yang, Y., & Zhou, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.195 · Cited by 757 · see citing works
SqueezeBERT: What can computer vision teach NLP about efficient neural networks?
Iandola, F., Shaw, A., Krishna, R., & Keutzer, K. (2020) · Proceedings of SustaiNLP: Workshop on Simple and Efficient Natural Language Processing · doi:10.18653/v1/2020.sustainlp-1.17 · Cited by 114 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A Survey of Techniques for Optimizing Transformer Inference
Chitty-Venkata, K. T., Mittal, S., Emani, M., Vishwanath, V., & Somani, A. K. (2023) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2023.102990 · Cited by 154 · see citing works
- Authors
- Chang, H.-Y., Mozafari, S. H., Chen, C., Clark, J. J., Meyer, B. H., & Gross, W. J.
- Affiliations
- McGill University (CA)
- Corresponding author
- Chang, Hung-Yang
- Mozafari, Seyyed Hasan
- Chen, Cheng
- Clark, James J.
- Meyer, Brett H.
- Gross, Warren J.
- ORCID
- Venue
- Journal of Signal Processing Systems
- Published
- 2022-10-08
- Volume
- 95
- Issue
- 7
- Pages
- 877-894
- ISSN
- 1939-8018 (ISSN-L)
- 1939-8115
- OpenAlex
- W4303645613
- Cited by
- 14
- References
- 4
- Citations by year
- 2025: 8 · 2024: 4 · 2023: 2
- FWCI
- 2.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Signal Processing Systems (publishedVersion)
- Funders
- Huawei Technologies
- Topics
- Physical Sciences › Computer Science › Hardware and Architecture › Parallel Computing and Optimization Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Hardware and Architecture › Embedded Systems Design Techniques
- Keywords
- ARM big.LITTLE, heterogeneous computing, edge inference, inference throughput, TVM, load balancing, energy-delay product, multi-core processors
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
