Combined HW/SW Drift and Variability Mitigation for PCM-based Analog In-memory Computing for Neural Network Applications

Alessio Antolini, Carmine Paolino, Francesco Zavalloni, Andrea Lico, Eleonora Franchi Scarselli, Mauro Mangia, Fabio Pareschi, Gianluca Setti, Riccardo Rovatti, Mattia Luigi Torres, Marcella Carissimi, Marco Pasotti

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Matrix-Vector Multiplications (MVMs) represent a heavy workload for both training and inference in Deep Neural Networks (DNNs) applications. Analog In-memory Computing (AIMC) systems based on Phase Change Memory (PCM) has been shown to be a valid competitor to enhance the energy efficiency of DNN accelerators. Although DNNs are quite resilient to computation inaccuracies, PCM non-idealities could strongly affect MVM operations precision, and thus the accuracy of DNNs. In this paper, a combined hardware and software solution to mitigate the impact of PCM non-idealities is presented. The drift of PCM cells conductance is compensated at the circuit level through the introduction of a conductance ratio at the core of the MVM computation. A model of the behaviour of PCM cells is employed to develop a device-aware training for DNNs and the accuracy is estimated in a CIFAR-10 classification task. This work is supported by a PCM-based AIMC prototype, designed in a 90-nm STMicroelectronics technology, and conceived to perform Multiply-and-Accumulate (MAC) computations, which are the kernel of MVMs. Results show that the MAC computation accuracy is around 95% even under the effect of cells drift. The use of a device-aware DNN training makes the networks less sensitive to weight variability, with a 15% increase in classification accuracy over a conventionally-trained Lenet-5 DNN, and a 36% gain when drift compensation is applied.
Original languageEnglish (US)
Pages (from-to)1-1
Number of pages1
JournalIEEE Journal on Emerging and Selected Topics in Circuits and Systems
StatePublished - Feb 2 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-02-06
Acknowledgements: This project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 101007321. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and France, Belgium, Czech Republic, Germany, Italy, Sweden, Switzerland, Turkey. The Authors wish to thank Chantal Auricchio and Laura Capecchi from STMicroelectronics Italy for their fundamental contribution to the testchip design and development.

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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