Compact Modeling and Mitigation of Parasitics in Crosspoint Accelerators of Neural Networks
In-memory computing (IMC) can accelerate data-intensive tasks, such as matrix-vector multiplication (MVM) or artificial neural networks (ANNs) inference, by means of the crosspoint memory array, allowing to reduce time and energy consumption. IMC accuracy, however, is affected by nonidealities, such...
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| Vydáno v: | IEEE transactions on electron devices Ročník 71; číslo 3; s. 1 - 7 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
IEEE
01.03.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0018-9383, 1557-9646 |
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| Abstract | In-memory computing (IMC) can accelerate data-intensive tasks, such as matrix-vector multiplication (MVM) or artificial neural networks (ANNs) inference, by means of the crosspoint memory array, allowing to reduce time and energy consumption. IMC accuracy, however, is affected by nonidealities, such as variability of the conductive weights or IR drop along wires due to parasitic resistances, whose impact steeply increases with the increase of array size. This work proposes a compact model to assess the impact of nonidealities for various circuital implementations, together with architectural schemes for their mitigation based on replicated arrays. The proposed mitigation techniques allow to restore the ANN accuracy from 72.7% to 94.9%, close to the software accuracy of 96.9%, in view of an increased area and energy consumption. |
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| AbstractList | In-memory computing (IMC) can accelerate data-intensive tasks, such as matrix-vector multiplication (MVM) or artificial neural networks (ANNs) inference, by means of the crosspoint memory array, allowing to reduce time and energy consumption. IMC accuracy, however, is affected by nonidealities, such as variability of the conductive weights or IR drop along wires due to parasitic resistances, whose impact steeply increases with the increase of array size. This work proposes a compact model to assess the impact of nonidealities for various circuital implementations, together with architectural schemes for their mitigation based on replicated arrays. The proposed mitigation techniques allow to restore the ANN accuracy from 72.7% to 94.9%, close to the software accuracy of 96.9%, in view of an increased area and energy consumption. |
| Author | Ielmini, D. Lepri, N. Mannocci, P. Porzani, M. Glukhov, A. |
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| References_xml | – volume: 214 start-page: 104 year: 2019 ident: ref8 article-title: Analysis of the statistics of device-to-device and cycle-to-cycle variability in TiN/Ti/Al:HfO2/TiN RRAMs publication-title: Microelectronic Eng. doi: 10.1016/j.mee.2019.05.004 – volume: 44 start-page: 1280 issue: 8 year: 2023 ident: ref12 article-title: Mitigate IR-drop effect by modulating neuron activation functions for implementing neural networks on memristor crossbar arrays publication-title: IEEE Electron Device Lett. doi: 10.1109/LED.2023.3285916 – ident: ref16 doi: 10.1109/5.726791 – ident: ref22 doi: 10.1109/TED.2021.3095433 – ident: ref7 doi: 10.1109/IEDM.2007.4419107 – ident: ref17 doi: 10.1109/ESSCIRC.2007.4430310 – ident: ref4 doi: 10.1109/iedm19573.2019.8993599 – ident: ref21 doi: 10.3389/fnins.2020.00634 – ident: ref1 doi: 10.1038/s41928-018-0092-2 – ident: ref2 doi: 10.3389/fnins.2016.00333 – ident: ref18 doi: 10.1109/TED.2021.3089995 – volume: 1 start-page: 52 issue: 1 year: 2018 ident: ref6 article-title: Analogue signal and image processing with large memristor crossbars publication-title: Nature Electron. doi: 10.1038/s41928-017-0002-z – ident: ref10 doi: 10.1116/1.1642639 – ident: ref9 doi: 10.1109/ted.2022.3169112 – start-page: 14 volume-title: IEDM Tech. Dig. ident: ref5 article-title: An analog neuro-optimizer with adaptable annealing based on 64 × 64 0T1R crossbar circuit – ident: ref11 doi: 10.1109/TED.2022.3141987 – ident: ref15 doi: 10.1109/IRPS48227.2022.9764486 – ident: ref19 doi: 10.1145/3316781.3317872 – ident: ref3 doi: 10.1109/ISSCC19947.2020.9062953 – ident: ref13 doi: 10.1002/advs.202105784 – year: 2014 ident: ref23 article-title: Adam: A method for stochastic optimization publication-title: arXiv:1412.6980 – volume: 12 start-page: 436 issue: 2 year: 2022 ident: ref14 article-title: Parasitic-aware modeling and neural network training scheme for energy-efficient processing-in-memory with resistive crossbar array publication-title: IEEE J. Emerg. Sel. Topics Circuits Syst. doi: 10.1109/JETCAS.2022.3172170 – ident: ref20 doi: 10.1109/TED.2018.2865352 |
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| SubjectTerms | Accuracy Arrays Artificial neural networks Deep learning emerging memory technologies Energy consumption hardware accelerator in-memory computing (IMC) Logic gates Mathematical analysis Matrix algebra Microprocessors Programming Resistance resistive switching memory (RRAM) Transistors Voltage Wires |
| Title | Compact Modeling and Mitigation of Parasitics in Crosspoint Accelerators of Neural Networks |
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