Analyzing Fault Tolerance Behaviour in Memristor-based Crossbar for Neuromorphic Applications

Yadav, Dev Narayan; Datta, Kamalika; Sengupta, Indranil

doi:10.1109/itcindia49857.2020.9171788

Cited by 9 publications

(1 citation statement)

References 32 publications

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“…The Hardware TF-IDF-based architectures are shown in figure 4. Each document represents sentences using a layered crossbar [15] that contains memristive crossbar nodes. The words in the document are converted into columns, where characters represent a single crossbar node.…”

Section: B Hardware Architecturementioning

confidence: 99%

Consumer Document Analytical Accelerator Hardware

Radhakrishnan¹,

Mahapatra²,

James³

2023

IEEE Access

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Document scanning devices are used for visual character recognition, followed by text analytics in the software. Often such character extraction is insecure, and any third party can manipulate the information. On the other hand, near-edge processing devices are restrained by limited resources and connectivity issues. The primary factors that lead to exploring independent hardware devices with natural language processing (NLP) capabilities are latency during cloud processing and computing costs. This paper introduces a hardware accelerator for information retrieval using memristive TF-IDF implementation. In this system, each sentence is represented using a memristive crossbar layer, with each column containing a single word. The number of matching scores for the TF and IDF values was implemented using operational amplifier-based comparator accumulator circuits. The circuit is designed with a 180nm CMOS process, Knowm Multi-Stable Switch memristor model, and WOx device parameters. We compared its performance with that of a standard benchmark dataset. Variability and device-to-device related issues were also taken into consideration in the analysis. This paper concludes with implementing TF-IDF score calculation for applications such as information retrieval and text summarization.

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Section: B Hardware Architecturementioning

confidence: 99%