Memristor-based Network Switching Architecture for Energy Efficient Cognitive Computational Models

Saleh, Saad; Koldehofe, Boris

doi:10.1145/3611315.3633272

Proceedings of the 18th ACM International Symposium on Nanoscale Architectures 2023

DOI: 10.1145/3611315.3633272

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Memristor-based Network Switching Architecture for Energy Efficient Cognitive Computational Models

Saad Saleh,

Boris Koldehofe

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2024

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Cited by 3 publications

References 9 publications

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Analog In-Network Computing through Memristor-based Match-Compute Processing

Saleh,

Goossens,

Shu

et al. 2024

IEEE INFOCOM 2024 - IEEE Conference on Computer Communications

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Current network functions consume a significant amount of energy and lack the capacity to support more expressive learning models like neuromorphic functions. The major reason is the underlying transistor-based components that require continuous energy-intensive data movements between the storage and computational units. In this research, we propose the use of a novel component, called Memristor, which can colocalize computation and storage, and provide computational capabilities. Building on memristors, we propose the concept of match-compute processing for supporting energy efficient network functions. Considering the analog processing of memristors, we propose a Probabilistic Content Addressable Memory (pCAM) abstraction which can provide analog match functions. pCAM provides deterministic and probabilistic outputs depending upon the closeness of match of an incoming query with the specified network policy. pCAM uses a crossbar array for line rate matrix multiplications on the match outputs. We proposed a match-compute packet processing architecture and developed the programming abstractions for a baseline network function, i.e., Active Queue Management, which drops packets based upon the higher-order derivatives of sojourn times and buffer sizes. The analysis of match-compute processing over a physically fabricated memristor chip showed only 0.01 fJ/bit/cell of energy consumption, which is 50 times less than the traditional match-action processing.

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Analog In-Network Computing through Memristor-based Match-Compute Processing

Saleh,

Goossens,

Shu

et al. 2024

IEEE INFOCOM 2024 - IEEE Conference on Computer Communications

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Adaptive In-Network Queue Management using Derivatives of Sojourn Time and Buffer Size

Saleh,

Shu,

Koldehofe

2024

NOMS 2024-2024 IEEE Network Operations and Management Symposium

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Active Queue Management (AQM) algorithms are heavily used in packet processors to maintain an optimal queue size and avoid issues like Bufferbloat. Despite the remarkable performance, the traditional AQM algorithms face a major challenge of estimating the accurate queue congestion due to bursty network conditions. The major reason is the use of baseline queue statistics for congestion estimation like delay and sojourn time for Random Early Detection (RED) and Controlled Delay (CoDel), respectively. In this paper, we propose a novel dAQM algorithm that uses advanced traffic statistics like three higher-order derivatives of sojourn time and buffer size along with the baseline sojourn time and buffer size for accurate congestion estimation. dAQM adjusts its drop rate based on the continuously varying congestion to cater to the needs of bursty traffic. We simulated dAQM in ns-3 and analyzed its performance for FTP traffic by variation in traffic load and packet sizes. The results showed that dAQM provides at least 25% and 39.7% reduction in packet loss ratio and flow completion time, respectively, as compared to the traditional AQM algorithms.

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dAQM: Derivative-Based Active Queue Management

Saleh,

Shu,

Koldehofe

2024

2024 IFIP Networking Conference (IFIP Networking)

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Memristor-based Network Switching Architecture for Energy Efficient Cognitive Computational Models

Cited by 3 publications

References 9 publications

Analog In-Network Computing through Memristor-based Match-Compute Processing

Analog In-Network Computing through Memristor-based Match-Compute Processing

Adaptive In-Network Queue Management using Derivatives of Sojourn Time and Buffer Size

dAQM: Derivative-Based Active Queue Management

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