Publikationen 2022

Script list Publications

(1) Statistical Model of Program/Verify Algorithms in Resistive Switching Memories for In-Memory Neural Network Accelerators
A. Glukhov, V. Milo, A. Baroni, N. Lepri, C. Zambelli, P. Olivo, E. Perez, Ch. Wenger, D. Ielmini
Proc. International Reliability Physics Symposium (IRPS 2022), 3C.3-1 (2022)
DOI: 10.1109/IRPS48227.2022.9764497, (KI-PRO)

(2) Statistical Model of Program/Verify Algorithms in Resistive Switching Memories for In-Memory Neural Network Accelerators
A. Glukhov, V. Milo, A. Baroni, N. Lepri, C. Zambelli, P. Olivo, E. Perez, Ch. Wenger, D. Ielmini
Proc. International Reliability Physics Symposium (IRPS 2022), 3C.3-1 (2022)
DOI: 10.1109/IRPS48227.2022.9764497, (Total Resilience)

(3) Statistical Model of Program/Verify Algorithms in Resistive Switching Memories for In-Memory Neural Network Accelerators
A. Glukhov, V. Milo, A. Baroni, N. Lepri, C. Zambelli, P. Olivo, E. Perez, Ch. Wenger, D. Ielmini
Proc. International Reliability Physics Symposium (IRPS 2022), 3C.3-1 (2022)
DOI: 10.1109/IRPS48227.2022.9764497, (NeuroMem)

(4) In-Memory Principal Component Analysis by Crosspoint Array of Rresistive Switching Memory: A New Hardware Approach for Energy-Efficient Data Analysis in Edge Computing
P. Mannocci, A. Baroni, E. Melacarne, C. Zambelli, P. Olivo, E. Perez, Ch. Wenger, D. Ielmini
IEEE Nanotechnology Magazine 16(2), 4 (2022)
DOI: 10.1109/MNANO.2022.3141515, (KI-IoT)
In-memory computing (IMC) is one of the most promising candidates for data-intensive computing accelerators of machine learning (ML). A key ML algorithm for dimensionality reduction and classification is the principal component analysis (PCA), which heavily relies on matrix-vector multiplications (MVM) for which classic von Neumann architectures are not optimized. Here, we provide the experimental demonstration of a new IMC-based PCA algorithm based on power iteration and deflation executed in a 4-kbit array of resistive switching randomaccess memory (RRAM). The classification accuracy of Wisconsin Breast Cancer dataset reaches 95.43%, close to floating-point implementation. Our simulations indicate a 250x improvement in energy efficiency compared to commercial graphic processing units (GPUs), thus supporting IMC for energy-efficient ML in modern data-intensive computing.

(5) In-Memory Principal Component Analysis by Crosspoint Array of Rresistive Switching Memory: A New Hardware Approach for Energy-Efficient Data Analysis in Edge Computing
P. Mannocci, A. Baroni, E. Melacarne, C. Zambelli, P. Olivo, E. Perez, Ch. Wenger, D. Ielmini
IEEE Nanotechnology Magazine 16(2), 4 (2022)
DOI: 10.1109/MNANO.2022.3141515, (Neutronics)
In-memory computing (IMC) is one of the most promising candidates for data-intensive computing accelerators of machine learning (ML). A key ML algorithm for dimensionality reduction and classification is the principal component analysis (PCA), which heavily relies on matrix-vector multiplications (MVM) for which classic von Neumann architectures are not optimized. Here, we provide the experimental demonstration of a new IMC-based PCA algorithm based on power iteration and deflation executed in a 4-kbit array of resistive switching randomaccess memory (RRAM). The classification accuracy of Wisconsin Breast Cancer dataset reaches 95.43%, close to floating-point implementation. Our simulations indicate a 250x improvement in energy efficiency compared to commercial graphic processing units (GPUs), thus supporting IMC for energy-efficient ML in modern data-intensive computing.

(6) In-Memory Principal Component Analysis by Crosspoint Array of Rresistive Switching Memory: A New Hardware Approach for Energy-Efficient Data Analysis in Edge Computing
P. Mannocci, A. Baroni, E. Melacarne, C. Zambelli, P. Olivo, E. Perez, Ch. Wenger, D. Ielmini
IEEE Nanotechnology Magazine 16(2), 4 (2022)
DOI: 10.1109/MNANO.2022.3141515, (Total Resilience)
In-memory computing (IMC) is one of the most promising candidates for data-intensive computing accelerators of machine learning (ML). A key ML algorithm for dimensionality reduction and classification is the principal component analysis (PCA), which heavily relies on matrix-vector multiplications (MVM) for which classic von Neumann architectures are not optimized. Here, we provide the experimental demonstration of a new IMC-based PCA algorithm based on power iteration and deflation executed in a 4-kbit array of resistive switching randomaccess memory (RRAM). The classification accuracy of Wisconsin Breast Cancer dataset reaches 95.43%, close to floating-point implementation. Our simulations indicate a 250x improvement in energy efficiency compared to commercial graphic processing units (GPUs), thus supporting IMC for energy-efficient ML in modern data-intensive computing.

(7) In-Depth Characterization of Switching Dynamics in Amorphous HfO2 Memristive Arrays for the Implementation of Synaptic Updating Rules
E. Perez, M.K. Mahadevaiah, E. Perez-Bosch Quesada, Ch. Wenger
Japanese Journal of Applied Physics 61(SM), SM1007 (2022)
DOI: 10.1109/TED.2021.3072868, (KI-IoT)
Accomplishing truly analog conductance modulation in memristive arrays is crucial in order to implement the synaptic plasticity in hardware-based neuromorphic systems. In this paper, such a feature was addressed by exploiting the inherent stochasticity of switching dynamics in amorphous HfO2 technology. A thorough statistical analysis of experimental characteristics measured in 4 kbit arrays by using trains of identical depression/ potentiation pulses with different voltage amplitudes and pulse widths provided the key to develop two different updating rules and to define their optimal programming parameters. The first rule is based on applying a specific number of identical pulses until the conductance value achieves the desired level. The second one utilized only one single pulse with a particular amplitude to achieve the targeted conductance level. In addition, all the results provided by the statistical analysis performed may play an important role in understanding better the switching behavior of this particular technology.

(8) In-Depth Characterization of Switching Dynamics in Amorphous HfO2 Memristive Arrays for the Implementation of Synaptic Updating Rules
E. Perez, M.K. Mahadevaiah, E. Perez-Bosch Quesada, Ch. Wenger
Japanese Journal of Applied Physics 61(SM), SM1007 (2022)
DOI: 10.1109/TED.2021.3072868, (NeuroMem)
Accomplishing truly analog conductance modulation in memristive arrays is crucial in order to implement the synaptic plasticity in hardware-based neuromorphic systems. In this paper, such a feature was addressed by exploiting the inherent stochasticity of switching dynamics in amorphous HfO2 technology. A thorough statistical analysis of experimental characteristics measured in 4 kbit arrays by using trains of identical depression/ potentiation pulses with different voltage amplitudes and pulse widths provided the key to develop two different updating rules and to define their optimal programming parameters. The first rule is based on applying a specific number of identical pulses until the conductance value achieves the desired level. The second one utilized only one single pulse with a particular amplitude to achieve the targeted conductance level. In addition, all the results provided by the statistical analysis performed may play an important role in understanding better the switching behavior of this particular technology.

(9) In-Depth Characterization of Switching Dynamics in Amorphous HfO2 Memristive Arrays for the Implementation of Synaptic Updating Rules
E. Perez, M.K. Mahadevaiah, E. Perez-Bosch Quesada, Ch. Wenger
Japanese Journal of Applied Physics 61(SM), SM1007 (2022)
DOI: 10.1109/TED.2021.3072868, (Neutronics)
Accomplishing truly analog conductance modulation in memristive arrays is crucial in order to implement the synaptic plasticity in hardware-based neuromorphic systems. In this paper, such a feature was addressed by exploiting the inherent stochasticity of switching dynamics in amorphous HfO2 technology. A thorough statistical analysis of experimental characteristics measured in 4 kbit arrays by using trains of identical depression/ potentiation pulses with different voltage amplitudes and pulse widths provided the key to develop two different updating rules and to define their optimal programming parameters. The first rule is based on applying a specific number of identical pulses until the conductance value achieves the desired level. The second one utilized only one single pulse with a particular amplitude to achieve the targeted conductance level. In addition, all the results provided by the statistical analysis performed may play an important role in understanding better the switching behavior of this particular technology.

(10) Laser Fault Injection Attacks against IHP Rad-Hard Techniques
D. Petryk, Z. Dyka
Proc. 34th Crypto-Day Matters 2022, (2022)
(Total Resilience)

(11) Distributed Artificial Intelligence as a Means to Achieve Self-X-Functions for Increasing Resilience: the First Steps
O. Shamilyan, I. Kabin, Z. Dyka, P. Langendoerfer
Proc. International Conference on Cyber-Physical Systems and Internet-of-Things (CPS & IoT 2022), 34 (2022)
DOI: 10.1109/MECO55406.2022.9797193, (Total Resilience)

Die Website ist für moderne Browser konzipiert. Bitte verwenden Sie einen aktuellen Browser.