TY - JOUR
T1 - Decoding Algorithms and HW Strategies to Mitigate Uncertainties in a PCM-Based Analog Encoder for Compressed Sensing
AU - Paolino, Carmine
AU - Antolini, Alessio
AU - Zavalloni, Francesco
AU - Lico, Andrea
AU - Franchi Scarselli, Eleonora
AU - Mangia, Mauro
AU - Marchioni, Alex
AU - Pareschi, Fabio
AU - Setti, Gianluca
AU - Rovatti, Riccardo
AU - Luigi Torres, Mattia
AU - Carissimi, Marcella
AU - Pasotti, Marco
N1 - KAUST Repository Item: Exported on 2023-02-22
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.
PY - 2023/2/13
Y1 - 2023/2/13
N2 - Analog In-Memory computing (AIMC) is a novel paradigm looking for solutions to prevent the unnecessary transfer of data by distributing computation within memory elements. One such operation is matrix-vector multiplication (MVM), a workhorse of many fields ranging from linear regression to Deep Learning. The same concept can be readily applied to the encoding stage in Compressed Sensing (CS) systems, where an MVM operation maps input signals into compressed measurements. With a focus on an encoder built on top of a Phase-Change Memory (PCM) AIMC platform, the effects of device non-idealities, namely programming spread and drift over time, are observed in terms of the reconstruction quality obtained for synthetic signals, sparse in the Discrete Cosine Transform (DCT) domain. PCM devices are simulated using statistical models summarizing the properties experimentally observed in an AIMC prototype, designed in a 90 nm STMicroelectronics technology. Different families of decoders are tested, and tradeoffs in terms of encoding energy are analyzed. Furthermore, the benefits of a hardware drift compensation strategy are also observed, highlighting its necessity to prevent the need for a complete reprogramming of the entire analog array. The results show >30 dB average reconstruction quality for mid-range conductances and a suitably selected decoder right after programming. Additionally, the hardware drift compensation strategy enables robust performance even when different drift conditions are tested.
AB - Analog In-Memory computing (AIMC) is a novel paradigm looking for solutions to prevent the unnecessary transfer of data by distributing computation within memory elements. One such operation is matrix-vector multiplication (MVM), a workhorse of many fields ranging from linear regression to Deep Learning. The same concept can be readily applied to the encoding stage in Compressed Sensing (CS) systems, where an MVM operation maps input signals into compressed measurements. With a focus on an encoder built on top of a Phase-Change Memory (PCM) AIMC platform, the effects of device non-idealities, namely programming spread and drift over time, are observed in terms of the reconstruction quality obtained for synthetic signals, sparse in the Discrete Cosine Transform (DCT) domain. PCM devices are simulated using statistical models summarizing the properties experimentally observed in an AIMC prototype, designed in a 90 nm STMicroelectronics technology. Different families of decoders are tested, and tradeoffs in terms of encoding energy are analyzed. Furthermore, the benefits of a hardware drift compensation strategy are also observed, highlighting its necessity to prevent the need for a complete reprogramming of the entire analog array. The results show >30 dB average reconstruction quality for mid-range conductances and a suitably selected decoder right after programming. Additionally, the hardware drift compensation strategy enables robust performance even when different drift conditions are tested.
UR - http://hdl.handle.net/10754/688158
UR - https://www.mdpi.com/2079-9268/13/1/17
U2 - 10.3390/jlpea13010017
DO - 10.3390/jlpea13010017
M3 - Article
SN - 2079-9268
VL - 13
SP - 17
JO - Journal of Low Power Electronics and Applications
JF - Journal of Low Power Electronics and Applications
IS - 1
ER -