Reliability aspects of binary vector-matrix-multiplications using ReRAM devices. Issue 3 (1st September 2022)
- Record Type:
- Journal Article
- Title:
- Reliability aspects of binary vector-matrix-multiplications using ReRAM devices. Issue 3 (1st September 2022)
- Main Title:
- Reliability aspects of binary vector-matrix-multiplications using ReRAM devices
- Authors:
- Bengel, Christopher
Mohr, Johannes
Wiefels, Stefan
Singh, Abhairaj
Gebregiorgis, Anteneh
Bishnoi, Rajendra
Hamdioui, Said
Waser, Rainer
Wouters, Dirk
Menzel, Stephan - Abstract:
- Abstract: Computation-in-memory using memristive devices is a promising approach to overcome the performance limitations of conventional computing architectures introduced by the von Neumann bottleneck which are also known as memory wall and power wall. It has been shown that accelerators based on memristive devices can deliver higher energy efficiencies and data throughputs when compared with conventional architectures. In the vast multitude of memristive devices, bipolar resistive switches based on the valence change mechanism (VCM) are particularly interesting due to their low power operation, non-volatility, high integration density and their CMOS compatibility. While a wide range of possible applications is considered, many of them such as artificial neural networks heavily rely on vector-matrix-multiplications (VMMs) as a mathematical operation. These VMMs are made up of large numbers of multiplication and accumulation (MAC) operations. The MAC operation can be realised using memristive devices in an analog fashion using Ohm's law and Kirchhoff's law. However, VCM devices exhibit a range of non-idealities, affecting the VMM performance, which in turn impacts the overall accuracy of the application. Those non-idealities can be classified into time-independent (programming variability) and time-dependent (read disturb and read noise). Additionally, peripheral circuits such as analog to digital converters can introduce errors during the digitalization. In this work, weAbstract: Computation-in-memory using memristive devices is a promising approach to overcome the performance limitations of conventional computing architectures introduced by the von Neumann bottleneck which are also known as memory wall and power wall. It has been shown that accelerators based on memristive devices can deliver higher energy efficiencies and data throughputs when compared with conventional architectures. In the vast multitude of memristive devices, bipolar resistive switches based on the valence change mechanism (VCM) are particularly interesting due to their low power operation, non-volatility, high integration density and their CMOS compatibility. While a wide range of possible applications is considered, many of them such as artificial neural networks heavily rely on vector-matrix-multiplications (VMMs) as a mathematical operation. These VMMs are made up of large numbers of multiplication and accumulation (MAC) operations. The MAC operation can be realised using memristive devices in an analog fashion using Ohm's law and Kirchhoff's law. However, VCM devices exhibit a range of non-idealities, affecting the VMM performance, which in turn impacts the overall accuracy of the application. Those non-idealities can be classified into time-independent (programming variability) and time-dependent (read disturb and read noise). Additionally, peripheral circuits such as analog to digital converters can introduce errors during the digitalization. In this work, we experimentally and theoretically investigate the impact of device- and circuit-level effects on the VMM in a VCM crossbars. Our analysis shows that the variability of the low resistive state plays a key role and that reading in the RESET direction should be favored to reading in the SET direction. … (more)
- Is Part Of:
- Neuromorphic computing and engineering. Volume 2:Issue 3(2022)
- Journal:
- Neuromorphic computing and engineering
- Issue:
- Volume 2:Issue 3(2022)
- Issue Display:
- Volume 2, Issue 3 (2022)
- Year:
- 2022
- Volume:
- 2
- Issue:
- 3
- Issue Sort Value:
- 2022-0002-0003-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-09-01
- Subjects:
- computing in memory -- memristor -- ReRAM -- circuit design -- compact modelling -- vector-matrix-multiplication -- dot-product
Neural networks (Computer science) -- Periodicals
Neural computers -- Periodicals
Neuromorphics -- Periodicals
006.3 - Journal URLs:
- http://www.iop.org/ ↗
https://iopscience.iop.org/journal/2634-4386 ↗ - DOI:
- 10.1088/2634-4386/ac6d04 ↗
- Languages:
- English
- ISSNs:
- 2634-4386
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
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- British Library DSC - BLDSS-3PM
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