Publications 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)
(Total Resilience)

(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)
(NeuroMem)

(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)
(KI-PRO)

(4) In-Memory Principal Component Analysis by Crosspoint Array of Rresistive Switching Memory
P. Mannocci, A. Baroni, E. Melacarne, C. Zambelli, P. Olivo, E. Perez, Ch. Wenger, D. Ielmini
IEEE Nanotechnology Magazine 4 (2022)
DOI: 10.1109/TED.2021.3089995, (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 250 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
P. Mannocci, A. Baroni, E. Melacarne, C. Zambelli, P. Olivo, E. Perez, Ch. Wenger, D. Ielmini
IEEE Nanotechnology Magazine 4 (2022)
DOI: 10.1109/TED.2021.3089995, (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 250 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
P. Mannocci, A. Baroni, E. Melacarne, C. Zambelli, P. Olivo, E. Perez, Ch. Wenger, D. Ielmini
IEEE Nanotechnology Magazine 4 (2022)
DOI: 10.1109/TED.2021.3089995, (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 250 improvement in energy efficiency compared to commercial graphic processing units (GPUs), thus supporting IMC for energy-efficient ML in modern data-intensive computing.

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