ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Neuromorphic In-Memory Computing Framework using Memtransistor Cross-bar based Support Vector Machines

Kumar, P and Nair, AR and Chatterjee, O and Paul, T and Ghosh, A and Chakrabartty, S and Thakur, CS (2019) Neuromorphic In-Memory Computing Framework using Memtransistor Cross-bar based Support Vector Machines. In: 62nd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2019, 4 - 7 August 2019, Dallas, pp. 311-314.

MWSCAS_2019.pdf - Published Version

Download (1MB) | Preview
Official URL: https://doi.org/10.1109/MWSCAS.2019.8885180


This paper presents a novel framework for designing support vector machines (SVMs), which does not impose restriction on the SVM kernel to be positive-definite and allows the user to define memory constraint in terms of fixed template vectors. This makes the framework scalable and enables its implementation for low-power, high-density and memory constrained embedded application. An efficient hardware implementation of the same is also discussed, which utilizes novel low power memtransistor based cross-bar architecture, and is robust to device mismatch and randomness. We used memtransistor measurement data, and showed that the designed SVMs can achieve classification accuracy comparable to traditional SVMs on both synthetic and real-world benchmark datasets. This framework would be beneficial for design of SVM based wake-up systems for internet of things (IoTs) and edge devices where memtransistors can be used to optimize system's energy-efficiency and perform in-memory matrix-vector multiplication (MVM).

Item Type: Conference Paper
Publication: Midwest Symposium on Circuits and Systems
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to the Authors.
Keywords: Classification (of information); Energy efficiency; Vectors; Wakes, Classification accuracy; Computing frameworks; Embedded application; Hardware implementations; Internet of thing (IoTs); memtransistor; Support vector machine (SVMs); Wake up, Support vector machines
Department/Centre: Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Division of Interdisciplinary Sciences > Centre for Nano Science and Engineering
Division of Physical & Mathematical Sciences > Physics
Date Deposited: 21 Oct 2022 08:52
Last Modified: 21 Oct 2022 08:52
URI: https://eprints.iisc.ac.in/id/eprint/77476

Actions (login required)

View Item View Item