All-Digital Computing-in-Memory Macro Supporting FP64-Based Fused Multiply-Add Operation Article Swipe
Dejian Li
,
Kefan Mo
,
Liang Liu
,
Biao Pan
,
Weili Li
,
Wang Kang
,
Lei Li
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13074085
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/app13074085
Recently, frequent data movement between computing units and memory during floating-point arithmetic has become a major problem for scientific computing. Computing-in-memory (CIM) is a novel computing paradigm that merges computing logic into memory, which can address the data movement problem with excellent power efficiency. However, the previous CIM paradigm failed to support double-precision floating-point format (FP64) due to its computing complexity. This paper presents a novel all-digital CIM macro-DCIM-FF to complete FP64 based fused multiply-add (FMA) operation for the first time. With 16 sub-CIM cells integrating digital multipliers to complete mantissa multiplication, DCIM-FF is able to provide correct rounded implementations for normalized/denormalized inputs in round-to-nearest-even mode and round-to-zero mode, respectively. To evaluate our design, we synthesized and tested the DCIM-FF macro in 55-nm CMOS technology. With a minimum power efficiency of 0.12 mW and a maximum computing efficiency of 26.9 TOPS/W, we successfully demonstrated that DCIM-FF can run the FP64-based FMA operation without error. Compared to related works, the proposed DCIM-FF macro shows significant power efficiency improvement and less area overhead based on CIM technology. This work paves a novel pathway for high-performance implementation of an FP64-based matrix-vector multiplication (MVM) operation, which is essential for hyperscale scientific computing.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/app13074085
- https://www.mdpi.com/2076-3417/13/7/4085/pdf?version=1679556567
- OA Status
- gold
- Cited By
- 4
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4360615553
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4360615553Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/app13074085Digital Object Identifier
- Title
-
All-Digital Computing-in-Memory Macro Supporting FP64-Based Fused Multiply-Add OperationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-23Full publication date if available
- Authors
-
Dejian Li, Kefan Mo, Liang Liu, Biao Pan, Weili Li, Wang Kang, Lei LiList of authors in order
- Landing page
-
https://doi.org/10.3390/app13074085Publisher landing page
- PDF URL
-
https://www.mdpi.com/2076-3417/13/7/4085/pdf?version=1679556567Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3417/13/7/4085/pdf?version=1679556567Direct OA link when available
- Concepts
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Computer science, Macro, Overhead (engineering), Multiplication (music), CMOS, Parallel computing, Computer hardware, Arithmetic, Computer engineering, Electronic engineering, Mathematics, Engineering, Programming language, Combinatorics, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
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2024: 4Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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