Fundamental Limits on Energy-Delay-Accuracy of In-memory Architectures\n in Inference Applications Article Swipe
Sujan K. Gonugondla
,
Charbel Sakr
,
Hassan Dbouk
,
Naresh R. Shanbhag
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2012.13645
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2012.13645
This paper obtains fundamental limits on the computational precision of\nin-memory computing architectures (IMCs). An IMC noise model and associated SNR\nmetrics are defined and their interrelationships analyzed to show that the\naccuracy of IMCs is fundamentally limited by the compute SNR\n($\\text{SNR}_{\\text{a}}$) of its analog core, and that activation, weight and\noutput precision needs to be assigned appropriately for the final output SNR\n$\\text{SNR}_{\\text{T}} \\rightarrow \\text{SNR}_{\\text{a}}$. The minimum\nprecision criterion (MPC) is proposed to minimize the ADC precision. Three\nin-memory compute models - charge summing (QS), current summing (IS) and charge\nredistribution (QR) - are shown to underlie most known IMCs. Noise, energy and\ndelay expressions for the compute models are developed and employed to derive\nexpressions for the SNR, ADC precision, energy, and latency of IMCs. The\ncompute SNR expressions are validated via Monte Carlo simulations in a 65 nm\nCMOS process. For a 512 row SRAM array, it is shown that: 1) IMCs have an upper\nbound on their maximum achievable $\\text{SNR}_{\\text{a}}$ due to constraints on\nenergy, area and voltage swing, and this upper bound reduces with technology\nscaling for QS-based architectures; 2) MPC enables $\\text{SNR}_{\\text{T}}\n\\rightarrow \\text{SNR}_{\\text{a}}$ to be realized with minimal ADC precision;\n3) QS-based (QR-based) architectures are preferred for low (high) compute SNR\nscenarios.\n
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2012.13645
- https://arxiv.org/pdf/2012.13645
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4287548627
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https://openalex.org/W4287548627Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2012.13645Digital Object Identifier
- Title
-
Fundamental Limits on Energy-Delay-Accuracy of In-memory Architectures\n in Inference ApplicationsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
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2020-12-25Full publication date if available
- Authors
-
Sujan K. Gonugondla, Charbel Sakr, Hassan Dbouk, Naresh R. ShanbhagList of authors in order
- Landing page
-
https://arxiv.org/abs/2012.13645Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2012.13645Direct 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
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https://arxiv.org/pdf/2012.13645Direct OA link when available
- Concepts
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Computer science, Energy (signal processing), Algorithm, Upper and lower bounds, Monte Carlo method, Static random-access memory, Scaling, Inference, Physics, Electronic engineering, Mathematics, Computer hardware, Artificial intelligence, Statistics, Quantum mechanics, Geometry, Engineering, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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| abstract_inverted_index.precision | 8, 48 |
| abstract_inverted_index.preferred | 185 |
| abstract_inverted_index.validated | 121 |
| abstract_inverted_index.(QR-based) | 182 |
| abstract_inverted_index.achievable | 149 |
| abstract_inverted_index.and\ndelay | 95 |
| abstract_inverted_index.associated | 18 |
| abstract_inverted_index.precision, | 111 |
| abstract_inverted_index.precision. | 71 |
| abstract_inverted_index.activation, | 45 |
| abstract_inverted_index.and\noutput | 47 |
| abstract_inverted_index.constraints | 153 |
| abstract_inverted_index.expressions | 96, 119 |
| abstract_inverted_index.fundamental | 3 |
| abstract_inverted_index.on\nenergy, | 154 |
| abstract_inverted_index.simulations | 125 |
| abstract_inverted_index.SNR\nmetrics | 19 |
| abstract_inverted_index.The\ncompute | 117 |
| abstract_inverted_index.\\rightarrow | 59 |
| abstract_inverted_index.upper\nbound | 145 |
| abstract_inverted_index.appropriately | 53 |
| abstract_inverted_index.architectures | 11, 183 |
| abstract_inverted_index.computational | 7 |
| abstract_inverted_index.fundamentally | 33 |
| abstract_inverted_index.of\nin-memory | 9 |
| abstract_inverted_index.the\naccuracy | 29 |
| abstract_inverted_index.architectures; | 168 |
| abstract_inverted_index.precision;\n3) | 180 |
| abstract_inverted_index.Three\nin-memory | 72 |
| abstract_inverted_index.SNR\nscenarios.\n | 190 |
| abstract_inverted_index.interrelationships | 24 |
| abstract_inverted_index.minimum\nprecision | 62 |
| abstract_inverted_index.derive\nexpressions | 106 |
| abstract_inverted_index.technology\nscaling | 165 |
| abstract_inverted_index.charge\nredistribution | 83 |
| abstract_inverted_index.\\text{SNR}_{\\text{a}}$ | 173 |
| abstract_inverted_index.$\\text{SNR}_{\\text{a}}$ | 150 |
| abstract_inverted_index.\\text{SNR}_{\\text{a}}$. | 60 |
| abstract_inverted_index.SNR\n$\\text{SNR}_{\\text{T}} | 58 |
| abstract_inverted_index.SNR\n($\\text{SNR}_{\\text{a}}$) | 38 |
| abstract_inverted_index.$\\text{SNR}_{\\text{T}}\n\\rightarrow | 172 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8999999761581421 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.48865052 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |