The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Computing in memory with FeFETs
Abstract
Data transfer between a processor and memory frequently represents a bottleneck with respect to improving application-level performance. Computing in memory (CiM), where logic and arithmetic operations are performed in memory, could significantly reduce both energy consumption and computational overheads associated with data transfer. Compact, low-power, and fast CiM designs could ultimately lead to improved application-level performance. This paper introduces a CiM architecture based on ferroelectric field effect transistors (FeFETs). The CiM design can serve as a general purpose, random access memory (RAM), and can also perform Boolean operations ((N)AND, (N)OR, X(N)OR, INV) as well as addition (ADD) between words in memory. Unlike existing CiM designs based on other emerging technologies, FeFET-CiM accomplishes the aforementioned operations via a single current reference in the sense amplifier, which leads to more compact designs and lower power. Furthermore, the high Ion/Ioff ratio of FeFETs enables an inexpensive voltage-based sense scheme. Simulation-based case studies suggest that our FeFET-CiM can achieve speed-ups (and energy reduction) of ~119X (~1.6X) and ~1.97X (~1.5X) over ReRAM and STT-RAM CiM designs with respect to in-memory addition of 32-bit words. Furthermore, our approach offers an average speedup of ~2.5X and energy reduction of ~1.7X when compared to a conventional (not in-memory) approach across a wide range of benchmarks.
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8-bit integer operations, or even fewer bits, offer sufficient precision for training and deploying neural networks across diverse applications.
“We chose this standard because 8-bit integer operations, or even fewer bits [219, 221], offer sufficient precision for training and deploying neural networks across diverse applications.”
From Brain-inspired computing systems: a systematic literature review (2024) · cites this paper as [219]
Citations
Cites
Temporal correlation detection using computational phase-change memory
Sebastian, A., Tuma, T., Papandreou, N., Le Gallo, M., Kull, L., Parnell, T., & Eleftheriou, E. (2017) · Nature Communications · doi:10.1038/s41467-017-01481-9 · Cited by 241
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Cited by
Sorted by citations
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
“We chose this standard because 8-bit integer operations, or even fewer bits [219, 221], offer sufficient precision for training and deploying neural networks across diverse applications.”
Method
Metadata
- Authors
- Reis, D., Niemier, M., & Hu, X. S.
- Affiliations
- University of Notre Dame (US)
- Venue
- Proceedings of the International Symposium on Low Power Electronics and Design
- Publisher
- ACM
- Published
- 2018-07-23
- Pages
- 1-6
- OpenAlex
- W2889210320
- Cited by
- 102
- References
- 1
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 15 · 2023: 21 · 2022: 15 · 2021: 13 · 2020: 10 · 2019: 12 · 2018: 2
- FWCI
- 14.76
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Symposium on Low Power Electronics and Design (ISLPED) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Low-power high-performance VLSI design
- Keywords
- in-memory computing, ferroelectric field-effect transistors, sense amplifier, energy efficiency, data transfer bottleneck
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