The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Deep Convolutional Neural Network Architecture With Reconfigurable Computation Patterns
Abstract
Deep convolutional neural networks (DCNNs) have been successfully used in many computer vision tasks. Previous works on DCNN acceleration usually use a fixed computation pattern for diverse DCNN models, leading to imbalance between power efficiency and performance. We solve this problem by designing a DCNN acceleration architecture called deep neural architecture (DNA), with reconfigurable computation patterns for different models. The computation pattern comprises a data reuse pattern and a convolution mapping method. For massive and different layer sizes, DNA reconfigures its data paths to support a hybrid data reuse pattern, which reduces total energy consumption by 5.9~8.4 times over conventional methods. For various convolution parameters, DNA reconfigures its computing resources to support a highly scalable convolution mapping method, which obtains 93% computing resource utilization on modern DCNNs. Finally, a layer-based scheduling framework is proposed to balance DNA's power efficiency and performance for different DCNNs. DNA is implemented in the area of 16 mm2at 65 nm. On the benchmarks, it achieves 194.4 GOPS at 200 MHz and consumes only 479 mW. The system-level power efficiency is 152.9 GOPS/W (considering DRAM access power), which outperforms the state-of-the-art designs by one to two orders.
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Cites
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Cited by
Convolutional Neural Networks: A Survey
Krichen, M. (2023) · Computers · doi:10.3390/computers12080151 · Cited by 623
High-Performance FPGA-Based CNN Accelerator With Block-Floating-Point Arithmetic
Lian, X., Liu, Z., Song, Z., Dai, J., Zhou, W., & Ji, X. (2019) · IEEE Transactions on Very Large Scale Integration (VLSI) Systems · doi:10.1109/tvlsi.2019.2913958 · Cited by 162
Metadata
- Authors
- Tu, F., Yin, S., Ouyang, P., Tang, S., Liu, L., & Wei, S.
- Venue
- IEEE Transactions on Very Large Scale Integration (VLSI) Systems
- Published
- 2017-08
- Volume
- 25
- Issue
- 8
- Pages
- 2220-2233
- ISSN
- 1063-8210 (ISSN-L)
- 1557-9999
- OpenAlex
- W2605487586
- Cited by
- 319
- References
- 21
- FWCI
- 13.37
- Type
- journal article
- Language
- en
- Open access
- closed
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