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Author Correction: Kernel approximation using analogue in-memory computing

Julian Büchel
Giacomo Camposampiero
Abu Sebastian
Amendments and Corrections21 Jan 2025
Nature Machine Intelligence

Volume: 7, P: 328
The design of analogue in-memory computing tiles

This Perspective examines the design of non-volatile memristive analogue in-memory computing tiles for deep neural network accelerators, considering the challenges and opportunities associated with designing the different components and providing projections for how technology scaling may impact key design elements.

Abhairaj Singh
Manuel Le Gallo
Abu Sebastian
Reviews19 Dec 2025
Nature Electronics

Volume: 8, P: 1156-1169
Kernel approximation using analogue in-memory computing

A kernel approximation method that enables linear-complexity attention computation via analogue in-memory computing (AIMC) to deliver superior energy efficiency is demonstrated on a multicore AIMC chip.

Julian Büchel
Giacomo Camposampiero
Abu Sebastian
Research13 Dec 2024
Nature Machine Intelligence

Volume: 6, P: 1605-1615
Rapid learning with phase-change memory-based in-memory computing through learning-to-learn

The authors combine biologically-inspired learning techniques with neuromorphic hardware to implement an energy-efficient system that demonstrates rapid learning of unseen tasks across various domains.

Thomas Ortner
Horst Petschenig
Robert Legenstein
ResearchOpen Access01 Feb 2025
Nature Communications

Volume: 16, P: 1-16
The inherent adversarial robustness of analog in-memory computing

Adversarial attacks threaten deep neural networks. Here, authors show analog in-memory computing chips enhance robustness, attributed to stochastic noise properties. This is validated experimentally and in simulations with larger transformer models.

Corey Lammie
Julian Büchel
Abu Sebastian
ResearchOpen Access19 Feb 2025
Nature Communications

Volume: 16, P: 1-12
Efficient scaling of large language models with mixture of experts and 3D analog in-memory computing

This study shows a viable pathway to the efficient deployment of state-of-the-art large language models using mixture of experts on 3D analog in-memory computing hardware.

Julian Büchel
Athanasios Vasilopoulos
Abu Sebastian
Research08 Jan 2025
Nature Computational Science

Volume: 5, P: 13-26
A 64-core mixed-signal in-memory compute chip based on phase-change memory for deep neural network inference

A multicore analogue in-memory computing chip that is designed and fabricated in 14 nm complementary metal–oxide–semiconductor technology with backend-integrated phase-change memory can be used for deep neural network inference.

Manuel Le Gallo
Riduan Khaddam-Aljameh
Abu Sebastian
Research10 Aug 2023
Nature Electronics

Volume: 6, P: 680-693
Deep learning software stacks for analogue in-memory computing-based accelerators

Analogue in-memory computing (AIMC), with digital processing, forms a useful architecture for performant end-to-end execution of deep neural network models, but requires the development of sophisticated software stacks. This Perspective outlines the challenges in designing deep learning software stacks for AIMC-based accelerators, and suggests directions for future research.

Corey Lammie
Hadjer Benmeziane
Abu Sebastian
Reviews02 Jul 2025
Nature Reviews Electrical Engineering

Volume: 2, P: 621-633
Demonstration of 4-quadrant analog in-memory matrix multiplication in a single modulation

Manuel Le Gallo
Oscar Hrynkevych
Abu Sebastian
ResearchOpen Access03 Oct 2024
npj Unconventional Computing

Volume: 1, P: 1-8

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