BinSparX: Sparsified Binary Neural Networks for Reduced Hardware Non-Idealities in Xbar Arrays Article Swipe
Akul Malhotra
,
Sumeet Kumar Gupta
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.03553
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2412.03553
Compute-in-memory (CiM)-based binary neural network (CiM-BNN) accelerators marry the benefits of CiM and ultra-low precision quantization, making them highly suitable for edge computing. However, CiM-enabled crossbar (Xbar) arrays are plagued with hardware non-idealities like parasitic resistances and device non-linearities that impair inference accuracy, especially in scaled technologies. In this work, we first analyze the impact of Xbar non-idealities on the inference accuracy of various CiM-BNNs, establishing that the unique properties of CiM-BNNs make them more prone to hardware non-idealities compared to higher precision deep neural networks (DNNs). To address this issue, we propose BinSparX, a training-free technique that mitigates non-idealities in CiM-BNNs. BinSparX utilizes the distinct attributes of BNNs to reduce the average current generated during the CiM operations in Xbar arrays. This is achieved by statically and dynamically sparsifying the BNN weights and activations, respectively (which, in the context of BNNs, is defined as reducing the number of +1 weights and activations). This minimizes the IR drops across the parasitic resistances, drastically mitigating their impact on inference accuracy. To evaluate our technique, we conduct experiments on ResNet-18 and VGG-small CiM-BNNs designed at the 7nm technology node using 8T-SRAM and 1T-1ReRAM. Our results show that BinSparX is highly effective in alleviating the impact of non-idealities, recouping the inference accuracy to near-ideal (software) levels in some cases and providing accuracy boost of up to 77.25%. These benefits are accompanied by energy reduction, albeit at the cost of mild latency/area increase.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2412.03553
- https://arxiv.org/pdf/2412.03553
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405090818
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405090818Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2412.03553Digital Object Identifier
- Title
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BinSparX: Sparsified Binary Neural Networks for Reduced Hardware Non-Idealities in Xbar ArraysWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-12-04Full publication date if available
- Authors
-
Akul Malhotra, Sumeet Kumar GuptaList of authors in order
- Landing page
-
https://arxiv.org/abs/2412.03553Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2412.03553Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2412.03553Direct OA link when available
- Concepts
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Binary number, Artificial neural network, Computer science, Computer hardware, Computational science, Computer architecture, Parallel computing, Embedded system, Artificial intelligence, Arithmetic, MathematicsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.resistances | 35 |
| abstract_inverted_index.sparsifying | 129 |
| abstract_inverted_index.accelerators | 6 |
| abstract_inverted_index.activations, | 134 |
| abstract_inverted_index.establishing | 65 |
| abstract_inverted_index.latency/area | 237 |
| abstract_inverted_index.resistances, | 161 |
| abstract_inverted_index.respectively | 135 |
| abstract_inverted_index.activations). | 152 |
| abstract_inverted_index.quantization, | 15 |
| abstract_inverted_index.technologies. | 46 |
| abstract_inverted_index.training-free | 95 |
| abstract_inverted_index.non-idealities | 32, 57, 78, 99 |
| abstract_inverted_index.non-idealities, | 204 |
| abstract_inverted_index.non-linearities | 38 |
| abstract_inverted_index.Compute-in-memory | 0 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile |