TY - GEN
T1 - Cross-layer FeFET Reliability Modeling for Robust Hyperdimensional Computing
AU - Kumar, Shubham
AU - Chatterjee, Swetaki
AU - Thomann, Simon
AU - Genssler, Paul R.
AU - Chauhan, Yogesh Singh
AU - Amrouch, Hussam
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Hyperdimensional computing (HDC) is an emerging learning paradigm that has gained a lot of attention due to its ability to train with fewer data, lightweight implementation, and resiliency against errors. Similar to the brain, HDC can learn patterns in one iteration from small training data by computing a similarity metric such as Hamming distance. Ferroelectric Field-Effect-Transistor (FeFET) based Ternary Content Addressable Memory (TCAM) has been demonstrated as an excellent candi-date for computing this similarity metric. However, variations in the underlying ferroelectric transistor does impact the reliable HDC operation. In this paper, we demonstrate an end-to-end cross-layer FeFET reliability modeling to obtain robust HDC across the computing stack starting from transistor physics all the way to circuits and systems. The effect of random spatial fluctuation of ferroelectric (FE) domains and other variability sources on electrical characteristics of FeFET is computed through detailed physics-based TCAD simulations. Then, the entire TCAM array is simulated in SPICE using a carefully designed and calibrated compact model to capture the effect of transistor variability on the error probability for individual Hamming distances. Finally, the error probability is employed to compute the loss of inference accuracy of HDC with a language recognition task. We observe very little loss in accuracy even with a high degree of variation.
AB - Hyperdimensional computing (HDC) is an emerging learning paradigm that has gained a lot of attention due to its ability to train with fewer data, lightweight implementation, and resiliency against errors. Similar to the brain, HDC can learn patterns in one iteration from small training data by computing a similarity metric such as Hamming distance. Ferroelectric Field-Effect-Transistor (FeFET) based Ternary Content Addressable Memory (TCAM) has been demonstrated as an excellent candi-date for computing this similarity metric. However, variations in the underlying ferroelectric transistor does impact the reliable HDC operation. In this paper, we demonstrate an end-to-end cross-layer FeFET reliability modeling to obtain robust HDC across the computing stack starting from transistor physics all the way to circuits and systems. The effect of random spatial fluctuation of ferroelectric (FE) domains and other variability sources on electrical characteristics of FeFET is computed through detailed physics-based TCAD simulations. Then, the entire TCAM array is simulated in SPICE using a carefully designed and calibrated compact model to capture the effect of transistor variability on the error probability for individual Hamming distances. Finally, the error probability is employed to compute the loss of inference accuracy of HDC with a language recognition task. We observe very little loss in accuracy even with a high degree of variation.
KW - Ferroelectric FET
KW - Hyper-dimensional Computing
KW - TCAM
KW - Variability
UR - http://www.scopus.com/inward/record.url?scp=85141994290&partnerID=8YFLogxK
U2 - 10.1109/VLSI-SoC54400.2022.9939626
DO - 10.1109/VLSI-SoC54400.2022.9939626
M3 - Conference contribution
AN - SCOPUS:85141994290
T3 - IEEE/IFIP International Conference on VLSI and System-on-Chip, VLSI-SoC
BT - Proceedings of the 2022 IFIP/IEEE 30th International Conference on Very Large Scale Integration, VLSI-SoC 2022
PB - IEEE Computer Society
T2 - 30th IFIP/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2022
Y2 - 3 October 2022 through 5 October 2022
ER -