In-memory computing (IMC) is a highly promising non-von Neumann computational paradigm that keeps alive the promise of achieving energy efficiencies of one femtoJoule per operation. The key idea is to perform certain computational tasks in place in memory, thereby obviating the need to shuttle data back and forth between the processing unit and memory. This is often achieved by exploiting the physical attributes of the memory devices, their array-level organization, etc. IMC derives inspiration from the highly entwined nature of computation and memory in the brain. Our group has been at the forefront of research on IMC spanning the areas of exploratory memory devices, mixed-signal circuit design, architectural exploration, algorithmic development as well as applications.

Deep learning has established itself as a powerful AI paradigm for a range of applications such as computer vision and language modeling. A key challenge for deep learning is its computational inefficiency, which could potentially be addressed by IMC. Attributes such as synaptic efficacy and plasticity can be implemented in place by exploiting the physical attributes of memory devices such as phase-change memory. We have designed and fabricated, in collaboration with other members of the IBM Research AI Hardware Center, the most advanced IMC compute cores for deep learning to date.

To further improve the energy efficiency and to achieve a more general AI, it is clear that one needs to transcend or augment deep learning. More bio-realistic deep learning and neuro-vector symbolic architectures are two highly promising approaches in this direction that could also benefit from IMC.

Selected Publications

Syed Ghazi Sarwat et al.,
Phase-change memtransistive synapses for mixed-plasticity neural computations,”
Nature Nanotechnology, 2022.

M. Hersche et al.,
Constrained Few-shot Class-incremental Learning,”
Conference on Computer Vision and Pattern Recognition (CVPR), 2022.

Syed Ghazi Sarwat et al.,
An integrated photonics engine for unsupervised correlation detection,”
Science Advances, 2022.

Mario Lanza et al.,
Memristive technologies for data storage, computation, encryption, and radio-frequency communication,”
Science, 2022.

Syed Ghazi Sarwat et al.,
Mechanism and Impact of Bipolar Current Voltage Asymmetry in Computational Phase- Change Memory,”
Advanced Materials, 2022.

G. Karunaratne et al.,
Robust high-dimensional memory-augmented neural networks,”
Nature Communications, 2021.

R. Khaddam-Aljameh et al.,
HERMES Core -- A 14nm CMOS and PCM-based In-Memory Compute Core using an array of 300ps/LSB Linearized CCO-based ADCs and local digital processing,”
Symposium on VLSI Circuits, 2021.

Syed Ghazi Sarwat et al.,
Projected Mushroom Type Phase-Change Memory,”
Advanced Functional Materials, 2021.

J. Feldmann et al.,
Parallel convolution processing using an integrated photonic tensor core,”
Nature, 2021.

I. Boybat et al.,
Temperature sensitivity of analog in-memory computing using phase-change memory,”
IEEE International Electron Devices Meeting (IEDM), 2021.

G. Karunaratne et al.,
In-memory hyperdimensional computing,”
Nature Electronics, 2020.

A. Sebastian et al.,
Memory devices and applications for in-memory computing,”
Nature Nanotechnology, 2020.

V. Joshi et al.,
Accurate deep neural network inference using computational phase-change memory,”
Nature Communications, 2020.

C. Rios et al.,
In-memory computing on a photonic platform,”
Science Advances, 2019 (open access).

A. Sebastian et al.,
Multi-level storage in phase-change memory devices,”
IBM RZ 3947, 2019.

S. Hamdioui et al.,
Applications of Computation-In-Memory Architectures based on Memristive Devices,”
Proc. Design, Automation and Test in Europe 2019 (DATE), 2019.

M. Le Gallo et al.,
Compressed Sensing With Approximate Message Passing Using In-Memory Computing,”
IEEE Trans. Electr. Dev. 65(10), 2018 (open access) IBM RZ 3944.

I. Boybat et al.,
Neuromorphic computing with multi-memristive synapses,”
Nature Communications 9, 2514, 2018 (open access), PDF.

A. Sebastian et al.,
Tutorial: Brain-inspired computing using phase-change memory devices,”
J. Appl. Phys. 124, 111101, 2018 (open access) IBM RZ 3946.

I. Giannopoulos et al.,
8-bit Precision In-Memory Multiplication with Projected Phase-Change Memory,”
Proc. IEDM, 2018 (not yet open due to embargo period).

M. Salinga et al.,
Monatomic phase change memory,”
Nature Materials 17, 681–685, 2018 (Cover) PDF (open access).

M. Le Gallo et al.,
Collective structural relaxation in phase-change memory devices,”
Adv. Electronic Materials 4(9), 2018, PDF.

M. Le Gallo et al.,
“Mixed-precision in-memory computing,”
Nature Electronics 1, 246–253, 2018 arXiv preprint arXiv:1701.04279 (open access) PDF.

N. Gong et al.,
Signal and noise extraction from analog memory elements for neuromorphic computing,”
Nature Communications 9(2102), 2018.

T. Moraitis et al.,
Spiking neural networks enable two-dimensional neurons and unsupervised multi-timescale learning,”
Int’l Joint Conference on Neural Networks (IJCNN), 2018.

N. Papandreou et al.,
Exploiting the non-linear current-voltage characteristics for resistive memory readout,”
Int’l Symposium on Circuits and Systems (ISCAS), 2018.

T. Moraitis et al.,
The role of short-term plasticity in neuromorphic learning,”
IEEE Nanotechnology Magazine 12(3), 45–53, 2018.

S. Woźniak et al., 
Deep Networks Incorporating Spiking Neural Dynamics,”
arXiv preprint arXiv:1812.07040, 2018.

S. Woźniak et al.,
Online Feature Learning from a non-iid Stream in a Neuromorphic System with Synaptic Competition,”
Joint Conference on Neural Networks (IJCNN), 2018.

S.R. Nandakumar et al.,
Mixed-precision training of deep neural networks using computational memory”,
arXiv preprint arXiv:1712.01192, 2017 (open access).

T.A. Bachmann et al.,
Memristive Effects in Oxygenated Amorphous Carbon Nanodevices,”
Nanotechnology 29(3), 2017.

S. Woźniak et al.,
Neuromorphic architecture with 1M memristive synapses for detection of weakly correlated inputs,”
IEEE Transactions on Circuits and Systems II: Express Briefs 64(11), 2017.

S. Woźniak et al.,
Neuromorphic system with phase-change synapses for pattern learning and feature extraction,”
Int’l Joint Conference on Neural Networks (IJCNN), 2017.

T. Moraitis et al.,
Fatiguing STDP: Learning from spike-timing codes in the presence of rate codes,”
Proc. Int’l Joint Conf. on Neural Networks (IJCNN), 2017.

S. Sidler et al.,
Unsupervised learning using phase-change synapses and complementary patterns,”
In: A. Lintas et al. (eds) Artificial Neural Networks and Machine Learning, ICANN 2017. Lecture Notes in Computer Science 10613. Springer, Cham, 2017.

A. Sebastian et al.,
Temporal correlation detection using computational phase-change memory,”
Nature Communications 8, article 1115, 2017.

M. Le Gallo et al.,
Mixed-precision in-memory computing,”
arXiv preprint arXiv:1701.04279, 2017.

G.W. Burr et al.,
Neuromorphic computing using non-volatile memory,”
Advances in Physics: X 2.1, 2017.

J. Secco et al.,
Flux-charge memristor model for phase change memory,”
IEEE Trans. Circuits and Systems II: Express Briefs, 2017.

T.A. Bachmann et al.,
Temperature evolution in nanoscale carbon-based memory devices due to local Joule heating”,
IEEE Trans. Nanotechnology 16(5), 806-811, 2017.

M. Le Gallo et al.,
Compressed sensing recovery using computational memory,”
Proc. IEEE Int’l. Electron Devices Meeting (IEDM), 2017.

S.R. Nandakumar et al.,
Supervised learning in spiking neural networks with MLC PCM synapses,”
75th Annual Device Research Conf. (DRC), 2017.

I. Boybat et al.,
Stochastic weight updates in phase-change memory-based synapses and their influence on artificial neural networks,”
13th Conf. on PhD Research in Microelectronics and Electronics (PRIME), 2017.

T. Tuma et al.,
Stochastic phase-change neurons,”
Nature Nanotechnology 11, 693-699, 2016.

T. Tuma et al.,
Detecting correlations using phase-change neurons and synapses,”
IEEE Elec. Dev. Lett. 37(9), 1238-1241, 2016.

A. Pantazi et al.,
All-memristive neuromorphic computing with level-tuned neurons,”
Nanotechnology 27(35), 355205, 2016.

M. Le Gallo et al.,
Evidence for thermally assisted threshold switching behavior in nanoscale phase-change memory cells,”
J. Appl. Phys. 119, 025704, 2016.

G.W. Burr et al.,
Recent progress in phase-change memory technology,”
IEEE J. Emerging and Selected Topics in Circuits and Systems 6(2), 146–162, 2016.

M. Le Gallo et al.,
The complete time/temperature dependence of I–V drift in PCM devices,”
Proc. 2016 IEEE Int’l Reliability Physics Symposium (IRPS), 2016.

M. Le Gallo et al.,
Inherent stochasticity in phase-change memory devices,”
Proc. European Solid-State Device Conf. (ESSDERC), 2016.

W.W. Koelmans et al.,
Carbon-based resistive memories,”
Proc. Int’l Memory Workshop (IMW), 2016.

S. Wozniak et al.,
Learning spatio-temporal patterns in the presence of input noise using phase-change memristors,”
Proc. Int’l Symposium on Circuits and Systems (ISCAS), 2016.

C.A. Santini et al.,
Oxygenated amorphous carbon for resistive memory applications,
Nature Communications 6, article 8600, 2015.

W.W. Koelmans et al.,
Projected phase-change memory devices,”
Nature Communications 6, article 8181, 2015.

M. Le Gallo et al.,
Subthreshold electrical transport in amorphous phase-change materials,”
New. J. Phys. 17, 093035, 2015.

P. Hosseini et al.,
Accumulation-based computing using phase change memories with FET access devices,”
IEEE Elec. Dev. Lett. 36(9), 975-977, 2015.

M. Kaes et al.,
High field electrical transport in amorphous phase-change materials,”
J. Appl. Phys. 118, 135707, 2015.

A. Sebastian et al.,
A collective relaxation model for resistance drift in phase change memory cells,”
Proc. 2015 IEEE Int’ Reliability Physics Symposium (IRPS), 2015.

A. Athmanathan et al.,
A finite-element thermoelectric model for phase-change memory devices,”
Proc. Int’l Conf. on Simulation of Semiconductor Processes and Devices (SISPAD), 2015.

A. Sebastian et al.,
Crystal growth within a phase change memory cell,”
Nature Communications 5, article 4314, 2014.