Low power high speed ternary content addressable memory design using MOSFET and memristors

Tabassum, Shawana; Parveen, Farhana; Rashid, A. B. M. H.

doi:10.1109/ecs.2014.6892672

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…The designs differed in the numbers of memristors and transistors employed to constitute the bit‐cell memory in the CAM array. Different bit‐cell arrangements of possible Re‐TCAM structures with SRAM 8T‐2M (8‐Transistors 2‐memristors), 24 SRAM‐6T‐2M, 25 SRAM‐5T‐2M, 26 Re‐CAM‐5T‐2M, 27 and Re‐CAM‐2T‐2M 28 were introduced. The 2T2M bit‐cell design is perceived as the densest and efficient circuit topology, primarily for Re‐TCAM.…”

Section: Introductionmentioning

confidence: 99%

Variability analysis of resistive ternary content addressable memories

Bahloul

Fouda

Barraj

et al. 2020

Circuit Theory & Apps

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Resistive ternary content addressable memories (Re-TCAMs) are considered a potential platform for in-memory associative processing. Re-TCAM permits the store and process of the data in the same physical area, allowing inmemory computing. Given the unique properties of the non-volatile memristive device, Re-TCAM is capable of low search energy, large-scale storage, and massively parallel processing. However, like any other semiconductor, memristor as a nanodevice suffers from fabrication process imperfections that provoke a significant variance in their high (R H) and low (R L) resistance states. These variations might lead to the malfunction of the Re-TCAM array and affect its performance and reliability. This paper proposes a simplified mathematical formulation of the memristor state variability for 2T2M bit-cell-based Re-TCAM array. We present a comprehensive statistical design, considering different circuit parameters and variance effects, such as tolerance of memristance variation, the memristor ratio (α = R H =R L), transistor technology, and memory width. The impact of these parameters on the performance is formulated as the probability of error is extensively studied, and a closed-form expression for the optimal threshold voltage is derived. The utility of the presented investigation is illustrated using a design example with real device parameters.

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Section: Introductionmentioning

confidence: 99%

Variability analysis of resistive ternary content addressable memories

Bahloul

Fouda

Barraj

et al. 2020

Circuit Theory & Apps

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“…Hybrid use of CMOS transistors and memristors is the most feasible option for practical implementation of memristors. In this regard, Tabassum et al [74] presented a memristorbased TCAM cell employing twelve transistors instead of sixteen, by using memristors for TCAM. The results suggest that the memristor-based TCAM consumes 70% less energy, 43% less search time and 27% less area.…”

Section: Cmos/memristor Hybrid Architecturementioning

confidence: 99%

“…Memristor storage [67][68] Memristor-based CAM [69] Test chip design [65] Cell electronics [66] CMOS-based hybrid architecture [74] Regular expression matching [71][72] Network intrusion detection [73] Range increase [108] Cognitive materials employment [99] Memristive Networks…”

Section: Memristive Tcammentioning

confidence: 99%

On Memristors for Enabling Energy Efficient and Enhanced Cognitive Network Functions

Saleh

Koldehofe

2022

IEEE Access

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The high performance requirements of nowadays computer networks are limiting their ability to support important requirements of the future. Two important properties essential in assuring cost-efficient computer networks and supporting new challenging network scenarios are operating energy efficient and supporting cognitive computational models. These requirements are hard to fulfill without challenging the current architecture behind network packet processing elements such as routers and switches. Notably, these are currently dominated by the use of traditional transistor-based components. In this article, we contribute with an in-depth analysis of alternative architectural design decisions to improve the energy footprint and computational capabilities of future network packet processors by shifting from transistor-based components to a novel component named Memristor. A memristor is a computational component characterized by non-volatile operations on a physical state, mostly represented in form of (electrical) resistance. Its state can be read or altered by input signals, e.g. electrical pulses, where the future state always depends on the past state. Unlike in traditional von Neumann architectures, the principles behind memristors impose that memory operations and computations are inherently colocated. In combination with the non-volatility, this allows to build memristors at nanoscale size and significantly reduce the energy consumption. At the same time, memristors appear to be highly suitable to model cognitive functionality due to the state dependence transitions in the memristor. In cognitive architectures, our survey contributes to the study of memristorbased Ternary Content Addressable Memory (TCAM) used for storage of cognitive rules inside packet processors. Moreover, we analyze the memristor-based novel cognitive computational architectures built upon self-learning capabilities by harnessing from non-volatility and state-based response of memristors (including reconfigurable architectures, reservoir computation architectures, neural network architectures and neuromorphic computing architectures).

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“…The authors verified the practicality and tolerance of the design through simulations and emulations in an FPGA platform. In [40], Tabassum et al focused on the design considerations of electronics requiring a hybrid setup of memristors and transistors. The authors proposed a TCAM cell design using twelve transistors coupled with memristors for write and search operations.…”

Section: Related Workmentioning

confidence: 99%

TCAmM^CogniGron: Energy Efficient Memristor-Based TCAM for Match-Action Processing

Saleh

Goossens

Banerjee

et al. 2022

2022 IEEE International Conference on Rebooting Computing (ICRC)

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The Internet relies heavily on programmable matchaction processors for matching network packets against locally available network rules and taking actions, such as forwarding and modification of network packets. This match-action process must be performed at high speed, i.e., commonly within one clock cycle, using a specialized memory unit called Ternary Content Addressable Memory (TCAM). Building on transistorbased CMOS designs, state-of-the-art TCAM architectures have high energy consumption and lack resilient designs for incorporating novel technologies for performing appropriate actions. In this article, we motivate the use of a novel fundamental component, the 'Memristor', for the development of TCAM architecture for match-action processing. Memristors can provide energy efficiency, non-volatility and better resource density as compared to transistors. We have proposed a novel memristorbased TCAM architecture called TCAmM CogniGron , built upon the voltage divider principle and requiring only two memristors and five transistors for storage and search operations compared to sixteen transistors in the traditional TCAM architecture. We analyzed its performance over an experimental data set of Nbdoped SrTiO 3 -based memristor. The analysis of TCAmM CogniGron showed promising power consumption statistics of 16 µW and 1 µW for match and mismatch operations along with twice the improvement in resources density as compared to the traditional architectures.

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Low power high speed ternary content addressable memory design using MOSFET and memristors

Cited by 6 publications

References 7 publications

Variability analysis of resistive ternary content addressable memories

Variability analysis of resistive ternary content addressable memories

On Memristors for Enabling Energy Efficient and Enhanced Cognitive Network Functions

TCAmM^CogniGron: Energy Efficient Memristor-Based TCAM for Match-Action Processing

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Low power high speed ternary content addressable memory design using MOSFET and memristors

Cited by 6 publications

References 7 publications

Variability analysis of resistive ternary content addressable memories

Variability analysis of resistive ternary content addressable memories

On Memristors for Enabling Energy Efficient and Enhanced Cognitive Network Functions

TCAmMCogniGron: Energy Efficient Memristor-Based TCAM for Match-Action Processing

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TCAmM^CogniGron: Energy Efficient Memristor-Based TCAM for Match-Action Processing