On the Reliability of In-Memory Computing: Impact of Temperature on Ferroelectric TCAM
Abstract
With the rapid development of emerging technologies, especially the ferroelectric field-effect transistors (FeFETs), the density and energy efficiency of ternary content addressable memory (TCAM) have been increasingly improved. TCAM plays a major role in realizing In-Memory Computing and other brain-inspired computing concepts. Recently, the parallel search functionality of a FeFET based ultra-dense TCAM design is also enhanced with a Hamming distance-based approximate search scheme. However, in order to realize the highly-promising TCAM design, in which the approximate search function based on Hamming distance is implemented, it is inevitable to investigate the impact of temperature on the reliability of FeFET-based TCAM cells as well as all involved peripheral circuits. In this paper, the temperature impact on the FeFET at the device level and the approximate TCAM design at the circuit level is investigated for the first time. The demonstrated example of a FeFET-based TCAM array shows that the unique temperature dependency of a FeFET device can help mitigate the temperature impact on the FeFET TCAM array. Based on the observation, we showcase, evaluate, and discuss in detail one strategy to eliminate the temperature impact on the approximate TCAM design. Understanding and mitigating the deleterious impact of temperature on the reliability of FeFET-based TCAM circuits is essential to ensure reliable In-Memory Computing.
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Related challenges for in-memory computing are achieving higher computational precision, inherent stochastic switching of devices and their impacts on reliability, and levels of parallelism.
“Related challenges are achieving higher computational precision [50, 51], inherent stochastic switching of devices and their impacts on reliability [52, 53], and levels of parallelism [54].”
From Brain-inspired computing systems: a systematic literature review (2024) · cites this paper as [52]
Cites
SearcHD: A Memory-Centric Hyperdimensional Computing With Stochastic Training
Imani, M., Yin, X., Messerly, J., Gupta, S., Niemier, M., Hu, X. S., & Rosing, T. (2020) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2019.2952544 · Cited by 87 · see citing works
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Brain-inspired computing systems: a systematic literature review
Zolfagharinejad, M., Alegre-Ibarra, U., Chen, T., Kinge, S., & van der Wiel, W. G. (2024) · The European Physical Journal B · doi:10.1140/epjb/s10051-024-00703-6 · Cited by 39 · see citing works
“Related challenges are achieving higher computational precision [50, 51], inherent stochastic switching of devices and their impacts on reliability [52, 53], and levels of parallelism [54].”
Background
- Authors
- Thomann, S., Li, C., Zhuo, C., Prakash, O., Yin, X., Hu, X. S., & Amrouch, H.
- Affiliations
- Karlsruhe Institute of Technology (DE)
- Zhejiang University (CN)
- University of Notre Dame (US)
- University of Stuttgart (DE)
- Venue
- 2021 IEEE 39th VLSI Test Symposium (VTS)
- Publisher
- IEEE
- Published
- 2021-04-25
- Pages
- 1-6
- ISSN
- 1093-0167 (ISSN-L)
- OpenAlex
- W3167846368
- Cited by
- 24
- References
- 1
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 5 · 2023: 6 · 2022: 8 · 2021: 2
- FWCI
- 3.82
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE VLSI Test Symposium (VTS) (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Keywords
- ferroelectric field-effect transistors, in-memory computing, temperature dependence, device reliability, approximate search, Hamming distance, parallel search, neuromorphic computing
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