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Jebali F, Majumdar A, Turck C, Harabi KE, Faye MC, Muhr E, Walder JP, Bilousov O, Michaud A, Vianello E, Hirtzlin T, Andrieu F, Bocquet M, Collin S, Querlioz D, Portal JM. Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell. Nat Commun 2024; 15:741. [PMID: 38272896 PMCID: PMC10811339 DOI: 10.1038/s41467-024-44766-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 01/04/2024] [Indexed: 01/27/2024] Open
Abstract
Memristor-based neural networks provide an exceptional energy-efficient platform for artificial intelligence (AI), presenting the possibility of self-powered operation when paired with energy harvesters. However, most memristor-based networks rely on analog in-memory computing, necessitating a stable and precise power supply, which is incompatible with the inherently unstable and unreliable energy harvesters. In this work, we fabricated a robust binarized neural network comprising 32,768 memristors, powered by a miniature wide-bandgap solar cell optimized for edge applications. Our circuit employs a resilient digital near-memory computing approach, featuring complementarily programmed memristors and logic-in-sense-amplifier. This design eliminates the need for compensation or calibration, operating effectively under diverse conditions. Under high illumination, the circuit achieves inference performance comparable to that of a lab bench power supply. In low illumination scenarios, it remains functional with slightly reduced accuracy, seamlessly transitioning to an approximate computing mode. Through image classification neural network simulations, we demonstrate that misclassified images under low illumination are primarily difficult-to-classify cases. Our approach lays the groundwork for self-powered AI and the creation of intelligent sensors for various applications in health, safety, and environment monitoring.
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Affiliation(s)
- Fadi Jebali
- Aix-Marseille Université, CNRS, Institut Matériaux Microélectronique Nanosciences de Provence, Marseille, France
| | - Atreya Majumdar
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France
| | - Clément Turck
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France
| | - Kamel-Eddine Harabi
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France
| | - Mathieu-Coumba Faye
- Aix-Marseille Université, CNRS, Institut Matériaux Microélectronique Nanosciences de Provence, Marseille, France
- Université Grenoble Alpes, CEA, LETI, Grenoble, France
| | - Eloi Muhr
- Aix-Marseille Université, CNRS, Institut Matériaux Microélectronique Nanosciences de Provence, Marseille, France
| | - Jean-Pierre Walder
- Aix-Marseille Université, CNRS, Institut Matériaux Microélectronique Nanosciences de Provence, Marseille, France
| | | | - Amadéo Michaud
- Institut Photovoltaïque d'Ile-de-France (IPVF), Palaiseau, France
| | | | | | | | - Marc Bocquet
- Aix-Marseille Université, CNRS, Institut Matériaux Microélectronique Nanosciences de Provence, Marseille, France
| | - Stéphane Collin
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France
- Institut Photovoltaïque d'Ile-de-France (IPVF), Palaiseau, France
| | - Damien Querlioz
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France.
| | - Jean-Michel Portal
- Aix-Marseille Université, CNRS, Institut Matériaux Microélectronique Nanosciences de Provence, Marseille, France.
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Bonnet D, Hirtzlin T, Majumdar A, Dalgaty T, Esmanhotto E, Meli V, Castellani N, Martin S, Nodin JF, Bourgeois G, Portal JM, Querlioz D, Vianello E. Bringing uncertainty quantification to the extreme-edge with memristor-based Bayesian neural networks. Nat Commun 2023; 14:7530. [PMID: 37985669 PMCID: PMC10661910 DOI: 10.1038/s41467-023-43317-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 11/07/2023] [Indexed: 11/22/2023] Open
Abstract
Safety-critical sensory applications, like medical diagnosis, demand accurate decisions from limited, noisy data. Bayesian neural networks excel at such tasks, offering predictive uncertainty assessment. However, because of their probabilistic nature, they are computationally intensive. An innovative solution utilizes memristors' inherent probabilistic nature to implement Bayesian neural networks. However, when using memristors, statistical effects follow the laws of device physics, whereas in Bayesian neural networks, those effects can take arbitrary shapes. This work overcome this difficulty by adopting a variational inference training augmented by a "technological loss", incorporating memristor physics. This technique enabled programming a Bayesian neural network on 75 crossbar arrays of 1,024 memristors, incorporating CMOS periphery for in-memory computing. The experimental neural network classified heartbeats with high accuracy, and estimated the certainty of its predictions. The results reveal orders-of-magnitude improvement in inference energy efficiency compared to a microcontroller or an embedded graphics processing unit performing the same task.
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Affiliation(s)
- Djohan Bonnet
- Université Grenoble Alpes, CEA, LETI, Grenoble, France.
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France.
| | | | - Atreya Majumdar
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France
| | | | | | | | | | - Simon Martin
- Université Grenoble Alpes, CEA, LETI, Grenoble, France
| | | | | | - Jean-Michel Portal
- Aix-Marseille Université, CNRS, Institut Matériaux Microélectronique Nanosciences de Provence, Marseille, France
| | - Damien Querlioz
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France.
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Abstract
While deep neural networks have surpassed human performance in multiple situations, they are prone to catastrophic forgetting: upon training a new task, they rapidly forget previously learned ones. Neuroscience studies, based on idealized tasks, suggest that in the brain, synapses overcome this issue by adjusting their plasticity depending on their past history. However, such "metaplastic" behaviors do not transfer directly to mitigate catastrophic forgetting in deep neural networks. In this work, we interpret the hidden weights used by binarized neural networks, a low-precision version of deep neural networks, as metaplastic variables, and modify their training technique to alleviate forgetting. Building on this idea, we propose and demonstrate experimentally, in situations of multitask and stream learning, a training technique that reduces catastrophic forgetting without needing previously presented data, nor formal boundaries between datasets and with performance approaching more mainstream techniques with task boundaries. We support our approach with a theoretical analysis on a tractable task. This work bridges computational neuroscience and deep learning, and presents significant assets for future embedded and neuromorphic systems, especially when using novel nanodevices featuring physics analogous to metaplasticity.
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Affiliation(s)
- Axel Laborieux
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France.
| | - Maxence Ernoult
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Saclay, Palaiseau, France
| | - Tifenn Hirtzlin
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France
| | - Damien Querlioz
- Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, Palaiseau, France.
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Hirtzlin T, Bocquet M, Penkovsky B, Klein JO, Nowak E, Vianello E, Portal JM, Querlioz D. Digital Biologically Plausible Implementation of Binarized Neural Networks With Differential Hafnium Oxide Resistive Memory Arrays. Front Neurosci 2020; 13:1383. [PMID: 31998059 PMCID: PMC6962102 DOI: 10.3389/fnins.2019.01383] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Accepted: 12/06/2019] [Indexed: 11/13/2022] Open
Abstract
The brain performs intelligent tasks with extremely low energy consumption. This work takes its inspiration from two strategies used by the brain to achieve this energy efficiency: the absence of separation between computing and memory functions and reliance on low-precision computation. The emergence of resistive memory technologies indeed provides an opportunity to tightly co-integrate logic and memory in hardware. In parallel, the recently proposed concept of a Binarized Neural Network, where multiplications are replaced by exclusive NOR (XNOR) logic gates, offers a way to implement artificial intelligence using very low precision computation. In this work, we therefore propose a strategy for implementing low-energy Binarized Neural Networks that employs brain-inspired concepts while retaining the energy benefits of digital electronics. We design, fabricate, and test a memory array, including periphery and sensing circuits, that is optimized for this in-memory computing scheme. Our circuit employs hafnium oxide resistive memory integrated in the back end of line of a 130-nm CMOS process, in a two-transistor, two-resistor cell, which allows the exclusive NOR operations of the neural network to be performed directly within the sense amplifiers. We show, based on extensive electrical measurements, that our design allows a reduction in the number of bit errors on the synaptic weights without the use of formal error-correcting codes. We design a whole system using this memory array. We show on standard machine learning tasks (MNIST, CIFAR-10, ImageNet, and an ECG task) that the system has inherent resilience to bit errors. We evidence that its energy consumption is attractive compared to more standard approaches and that it can use memory devices in regimes where they exhibit particularly low programming energy and high endurance. We conclude the work by discussing how it associates biologically plausible ideas with more traditional digital electronics concepts.
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Affiliation(s)
- Tifenn Hirtzlin
- C2N, Univ Paris-Sud, Université Paris-Saclay, CNRS, Palaiseau, France
| | - Marc Bocquet
- Aix Marseille Univ, Université de Toulon, CNRS, IM2NP, Marseille, France
| | - Bogdan Penkovsky
- C2N, Univ Paris-Sud, Université Paris-Saclay, CNRS, Palaiseau, France
| | | | | | | | - Jean-Michel Portal
- Aix Marseille Univ, Université de Toulon, CNRS, IM2NP, Marseille, France
| | - Damien Querlioz
- C2N, Univ Paris-Sud, Université Paris-Saclay, CNRS, Palaiseau, France
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Romera M, Talatchian P, Tsunegi S, Abreu Araujo F, Cros V, Bortolotti P, Trastoy J, Yakushiji K, Fukushima A, Kubota H, Yuasa S, Ernoult M, Vodenicarevic D, Hirtzlin T, Locatelli N, Querlioz D, Grollier J. Vowel recognition with four coupled spin-torque nano-oscillators. Nature 2018; 563:230-234. [PMID: 30374193 DOI: 10.1038/s41586-018-0632-y] [Citation(s) in RCA: 96] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2017] [Accepted: 07/31/2018] [Indexed: 11/10/2022]
Abstract
In recent years, artificial neural networks have become the flagship algorithm of artificial intelligence1. In these systems, neuron activation functions are static, and computing is achieved through standard arithmetic operations. By contrast, a prominent branch of neuroinspired computing embraces the dynamical nature of the brain and proposes to endow each component of a neural network with dynamical functionality, such as oscillations, and to rely on emergent physical phenomena, such as synchronization2-6, for solving complex problems with small networks7-11. This approach is especially interesting for hardware implementations, because emerging nanoelectronic devices can provide compact and energy-efficient nonlinear auto-oscillators that mimic the periodic spiking activity of biological neurons12-16. The dynamical couplings between oscillators can then be used to mediate the synaptic communication between the artificial neurons. One challenge for using nanodevices in this way is to achieve learning, which requires fine control and tuning of their coupled oscillations17; the dynamical features of nanodevices can be difficult to control and prone to noise and variability18. Here we show that the outstanding tunability of spintronic nano-oscillators-that is, the possibility of accurately controlling their frequency across a wide range, through electrical current and magnetic field-can be used to address this challenge. We successfully train a hardware network of four spin-torque nano-oscillators to recognize spoken vowels by tuning their frequencies according to an automatic real-time learning rule. We show that the high experimental recognition rates stem from the ability of these oscillators to synchronize. Our results demonstrate that non-trivial pattern classification tasks can be achieved with small hardware neural networks by endowing them with nonlinear dynamical features such as oscillations and synchronization.
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Affiliation(s)
- Miguel Romera
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Sud, Université Paris-Saclay, Palaiseau, France
| | - Philippe Talatchian
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Sud, Université Paris-Saclay, Palaiseau, France
| | - Sumito Tsunegi
- National Institute of Advanced Industrial Science and Technology (AIST), Spintronics Research Center, Tsukuba, Ibaraki, Japan
| | - Flavio Abreu Araujo
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Sud, Université Paris-Saclay, Palaiseau, France.,Institute of Condensed Matter and Nanosciences, UC Louvain, Louvain-la-Neuve, Belgium
| | - Vincent Cros
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Sud, Université Paris-Saclay, Palaiseau, France
| | - Paolo Bortolotti
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Sud, Université Paris-Saclay, Palaiseau, France
| | - Juan Trastoy
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Sud, Université Paris-Saclay, Palaiseau, France
| | - Kay Yakushiji
- National Institute of Advanced Industrial Science and Technology (AIST), Spintronics Research Center, Tsukuba, Ibaraki, Japan
| | - Akio Fukushima
- National Institute of Advanced Industrial Science and Technology (AIST), Spintronics Research Center, Tsukuba, Ibaraki, Japan
| | - Hitoshi Kubota
- National Institute of Advanced Industrial Science and Technology (AIST), Spintronics Research Center, Tsukuba, Ibaraki, Japan
| | - Shinji Yuasa
- National Institute of Advanced Industrial Science and Technology (AIST), Spintronics Research Center, Tsukuba, Ibaraki, Japan
| | - Maxence Ernoult
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Sud, Université Paris-Saclay, Palaiseau, France.,Centre de Nanosciences et de Nanotechnologies, CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, France
| | - Damir Vodenicarevic
- Centre de Nanosciences et de Nanotechnologies, CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, France
| | - Tifenn Hirtzlin
- Centre de Nanosciences et de Nanotechnologies, CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, France
| | - Nicolas Locatelli
- Centre de Nanosciences et de Nanotechnologies, CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, France
| | - Damien Querlioz
- Centre de Nanosciences et de Nanotechnologies, CNRS, Université Paris-Sud, Université Paris-Saclay, Orsay, France.
| | - Julie Grollier
- Unité Mixte de Physique, CNRS, Thales, Université Paris-Sud, Université Paris-Saclay, Palaiseau, France.
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Perez-Galacho D, Baudot C, Hirtzlin T, Messaoudène S, Vulliet N, Crozat P, Boeuf F, Vivien L, Marris-Morini D. Low voltage 25Gbps silicon Mach-Zehnder modulator in the O-band. Opt Express 2017; 25:11217-11222. [PMID: 28788803 DOI: 10.1364/oe.25.011217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In this work, a 25 Gb ps silicon push-pull Mach-Zehnder modulator operating in the O-Band (1260 nm - 1360 nm) of optical communications and fabricated on a 300 mm platform is presented. The measured modulation efficiency (VπLπ) was comprised between 0.95 V cm and 1.15 V cm, which is comparable to the state-of-the-art modulators in the C-Band, that enabled its use with a driving voltage of 3.3 Vpp, compatible with BiCMOS technology. An extinction ratio of 5 dB and an on-chip insertion losses of 3.6 dB were then demonstrated at 25 Gb ps.
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