VLCP: A High-Performance FPGA-based CNN Accelerator with Vector-level Cluster Pruning Article Swipe

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Shuo Ran , Bi Wu , Ke Chen , Weiqiang Liu ·

YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.1145/3611315.3633271

Convolutional neural networks (CNNs) are widely used in computer vision, natural language processing, and other application scenarios. But deploying CNNs at the edge is challenging due to their large number of parameters. Pruning is a solution that can effectively reduce the number of parameters and off-chip memory accesses. However, high sparsity unstructured pruning is not hardware-friendly, while structured pruning has low compression efficiency. As a result, vector-level pruning, with a coarser granularity, is a promising alternative that balances pruning performance and hardware-friendliness. In this paper, a hardware-oriented vector-level pruning strategy is proposed based on the CNN vector distribution properties. By expanding the dynamic range of vector groups, more important weights can be preserved without sacrificing accuracy. When applied to the VGG-16 and ResNet-18 models on the ImageNet dataset, the proposed strategy achieved 10.93 × and 10.17 × compression ratios in convolutional layers with a 66% reduction in computation and an acceptable drop in top-1 accuracy. Furthermore, the proposed pruning scheme achieves a remarkable performance of 188 FPS on the VCU118 evaluation board, demonstrating its compatibility with hardware. Compared to the state-of-the-art, the proposed strategy reaches 69% performance improvement and up to 2.8 × higher LUT efficiency.

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Computer Science

Pruning

Convolutional Neural Network

Parallel Computing

Field-Programmable Gate Array

Artificial Intelligence

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Computer science Pruning Convolutional neural network Parallel computing Computation Field-programmable gate array Reduction (mathematics) Granularity Artificial intelligence Pattern recognition (psychology) Computer engineering Computer hardware Algorithm Mathematics Agronomy Geometry Operating system Biology

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Language: en
Landing Page: https://doi.org/10.1145/3611315.3633271
PDF: https://dl.acm.org/doi/pdf/10.1145/3611315.3633271
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Title: VLCP: A High-Performance FPGA-based CNN Accelerator with Vector-level Cluster Pruning

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Publication year: 2023

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Publication date: 2023-12-18

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Authors: Shuo Ran, Bi Wu, Ke Chen, Weiqiang Liu

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Concepts: Computer science, Pruning, Convolutional neural network, Parallel computing, Computation, Field-programmable gate array, Reduction (mathematics), Granularity, Artificial intelligence, Pattern recognition (psychology), Computer engineering, Computer hardware, Algorithm, Mathematics, Agronomy, Geometry, Operating system, Biology

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abstract_inverted_index.accesses.	47
abstract_inverted_index.accuracy.	115, 154
abstract_inverted_index.deploying	18
abstract_inverted_index.expanding	100
abstract_inverted_index.hardware.	176
abstract_inverted_index.important	108
abstract_inverted_index.preserved	112
abstract_inverted_index.promising	74
abstract_inverted_index.reduction	145
abstract_inverted_index.acceptable	150
abstract_inverted_index.evaluation	170
abstract_inverted_index.parameters	43
abstract_inverted_index.remarkable	162
abstract_inverted_index.scenarios.	16
abstract_inverted_index.structured	57
abstract_inverted_index.alternative	75
abstract_inverted_index.application	15
abstract_inverted_index.challenging	24
abstract_inverted_index.compression	61, 137
abstract_inverted_index.computation	147
abstract_inverted_index.effectively	38
abstract_inverted_index.efficiency.	62, 195
abstract_inverted_index.improvement	187
abstract_inverted_index.parameters.	31
abstract_inverted_index.performance	79, 163, 186
abstract_inverted_index.processing,	12
abstract_inverted_index.properties.	98
abstract_inverted_index.sacrificing	114
abstract_inverted_index.Furthermore,	155
abstract_inverted_index.distribution	97
abstract_inverted_index.granularity,	71
abstract_inverted_index.unstructured	51
abstract_inverted_index.vector-level	66, 87
abstract_inverted_index.Convolutional	0
abstract_inverted_index.compatibility	174
abstract_inverted_index.convolutional	140
abstract_inverted_index.demonstrating	172
abstract_inverted_index.hardware-oriented	86
abstract_inverted_index.state-of-the-art,	180
abstract_inverted_index.hardware-friendly,	55
abstract_inverted_index.hardware-friendliness.	81
cited_by_percentile_year.max	95
cited_by_percentile_year.min	91
countries_distinct_count	1
institutions_distinct_count	4
citation_normalized_percentile.value	0.49921643
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False