Threshold Switch Modeling for Analog CAM Design

Jinane, Bazzi,; Fouda, Mohammed E.; Kanj, Rouwaida; Eltawil, Ahmed M.

doi:10.1109/icm50269.2020.9331775

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…We also set V DD =0.8V. We used the threshold switching model described in [24]. To derive the intervals and dynamic ranges, we implemented our algorithms using MATLAB [25].…”

Section: A Experimental Setupmentioning

confidence: 99%

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Variability-Aware Design of RRAM-Based Analog CAMs

Bazzi,

Sweidan,

Fouda

et al. 2024

IEEE Access

Self Cite

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Content Addressable Memories (CAMs) are considered a key enabler for in-memory computing (IMC). IMC shows an order of magnitude improvement in energy efficiency and throughput compared to traditional computing techniques. Recently, analog CAMs (aCAMs) were proposed as a means to improve storage density and energy efficiency. In this work, we propose two new variability-aware aCAM cells to improve data encoding and robustness against noise and process variations, as compared to existing aCAM cells. We propose a methodology to choose the margin and interval width for data encoding. In addition, we perform a comprehensive comparison against prior work in terms of the number of intervals, noise sensitivity, dynamic range, energy, latency, area, and probability of failure. The results show that our designs have the capability to encode up to 4 bits of storage and achieve up to 24 intervals compared to 6 intervals for the published 4T2M2S design, with 5-10× lower latency and 10-30× lower energy consumption, while achieving a lower probability of failure. This makes them suitable candidates for neuromorphic computing and routing applications as will be discussed in the results section.

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“…We also set V DD =0.8V. We used the threshold switching model described in [24]. To derive the intervals and dynamic ranges, we implemented our algorithms using MATLAB [25].…”

Section: A Experimental Setupmentioning

confidence: 99%

“…This prevents us from taking advantage of its steep switching threshold properly due to device variation. A study on the impact of the TS variability on the aCAM cell performance is conducted in an earlier work [24].…”

Section: H Memristor Variabilitymentioning

confidence: 99%

Variability-Aware Design of RRAM-Based Analog CAMs

Bazzi,

Sweidan,

Fouda

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…We also set V DD = 0.8V. We used the threshold switching model described in [21]. To derive the intervals and dynamic ranges, we implemented our algorithms using MATLAB [22].…”

Section: A Experimental Setupmentioning

confidence: 99%

Efficient Analog CAM Design

Jinane¹,

Sweidan²,

Fouda³

et al. 2022

Preprint

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Content Addressable Memories (CAMs) are considered a key-enabler for in-memory computing (IMC). IMC shows order of magnitude improvement in energy efficiency and throughput compared to traditional computing techniques. Recently, analog CAMs (aCAMs) were proposed as a means to improve storage density and energy efficiency. In this work, we propose two new aCAM cells to improve data encoding and robustness as compared to existing aCAM cells. We propose a methodology to choose the margin and interval width for data encoding. In addition, we perform a comprehensive comparison against prior work in terms of the number of intervals, noise sensitivity, dynamic range, energy, latency, area, and probability of failure.

show abstract

Threshold Switch Modeling for Analog CAM Design

Cited by 2 publications

References 7 publications

Variability-Aware Design of RRAM-Based Analog CAMs

Variability-Aware Design of RRAM-Based Analog CAMs

Efficient Analog CAM Design

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