Cited by 24Network Packet Processing and OptimizationFerroelectric and Negative Capacitance DevicesAdvanced Memory and Neural Computing

On the Reliability of In-Memory Computing: Impact of Temperature on Ferroelectric TCAM

Thomann, Simon, Li, Chao, Zhuo, Cheng, Prakash, Om, Yin, Xunzhao, Hu, Xiaobo Sharon, Amrouch, Hussam·2021·2021 IEEE 39th VLSI Test Symposium (VTS)

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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Crossref · 10.1109/vts50974.2021.9441038

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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].”

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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
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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