Machine Learning at the Edge

Verhelst, Marian; Murmann, Boris

doi:10.1007/978-3-030-18338-7_18

Cited by 12 publications

(7 citation statements)

References 48 publications

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“…The efficiency of the required calculation is such that trained NNs can be converted into compact and efficient TensorFlow Lite models, with a size of approximately 90 kB. These models are suitable for deployment and execution on microcontrollers, which would minimize resource demands and enable data collection and estimation on the same device, leading to a significant reduction in latency [49,50]. We can expect that, during data collection of photon-counting measurements, different chunks of measured time-delays are fed into the trained NN models for inference in real time.…”

Section: Computation Efficiencymentioning

confidence: 99%

“…Generally, the Bayesian parameter estimation method will be computationally time-consuming, even in simple systems. This hampers the prospects for real-time estimation and for the integration of the inference process in the actual measurement device taking the data, which would be desirable for reduced latency and energy consumption [49,50].…”

Section: Introductionmentioning

confidence: 99%

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Parameter estimation from quantum-jump data using neural networks

Rinaldi,

González Lastre,

García Herreros

et al. 2024

Quantum Sci. Technol.

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We present an inference method utilizing artificial neural networks for parameter estimation of a quantum probe monitored through a single continuous measurement. Unlike existing approaches focusing on the diffusive signals generated by continuous weak measurements, our method harnesses quantum correlations in discrete photon-counting data characterized by quantum jumps.We benchmark the precision of this method against Bayesian inference, which is optimal in the sense of information retrieval. By using numerical experiments on a two-level quantum system, we demonstrate that our approach can achieve a similar optimal performance as Bayesian inference, while drastically reducing computational costs. Additionally, the method exhibits robustness against the presence of imperfections in both measurement and training data. This approach offers a promising and computationally efficient tool for quantum parameter estimation with photon-counting data, relevant for applications such as quantum sensing or quantum imaging, as well as robust calibration tasks in laboratory-based settings.

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Section: Computation Efficiencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameter estimation from quantum-jump data using neural networks

Rinaldi,

González Lastre,

García Herreros

et al. 2024

Quantum Sci. Technol.

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“…This on-device processing capability is also a cornerstone of edge computing, where data are processed locally, enabling real-time data analysis and decision making. NPUs are instrumental in this paradigm, offering the necessary computational power to handle complex AI tasks at the edge, close to where data are generated [71,[93][94][95].…”

Section: Neural Processing Units (Npus)mentioning

confidence: 99%

Advancements in Artificial Intelligence Circuits and Systems (AICAS)

Miller,

Durlik,

Kostecka

et al. 2023

Electronics

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In the rapidly evolving landscape of electronics, Artificial Intelligence Circuits and Systems (AICAS) stand out as a groundbreaking frontier. This review provides an exhaustive examination of the advancements in AICAS, tracing its development from inception to its modern-day applications. Beginning with the foundational principles that underpin AICAS, we delve into the state-of-the-art architectures and design paradigms that are propelling the field forward. This review also sheds light on the multifaceted applications of AICAS, from optimizing energy efficiency in electronic devices to empowering next-generation cognitive computing systems. Key challenges, such as scalability and robustness, are discussed in depth, along with potential solutions and emerging trends that promise to shape the future of AICAS. By offering a comprehensive overview of the current state and potential trajectory of AICAS, this review serves as a valuable resource for researchers, engineers, and industry professionals looking to harness the power of AI in electronics.

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“…The concept of employing machine learning techniques directly on embedded end devices to perform on-device sensor data analytics, so-called edge machine learning, has recently gained a lot of attention (see [ 24 , 25 , 26 ] for reviews). It enables the development of intelligent sensor systems with extremely low power consumption that do not need to exchange data with a cloud server (where machine learning algorithms would traditionally be performed).…”

Section: Introductionmentioning

confidence: 99%

Edge-Machine-Learning-Assisted Robust Magnetometer Based on Randomly Oriented NV-Ensembles in Diamond

Homrighausen

Horsthemke

Pogorzelski

et al. 2023

Sensors

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Quantum magnetometry based on optically detected magnetic resonance (ODMR) of nitrogen vacancy centers in nano- or micro-diamonds is a promising technology for precise magnetic-field sensors. Here, we propose a new, low-cost and stand-alone sensor setup that employs machine learning on an embedded device, so-called edge machine learning. We train an artificial neural network with data acquired from a continuous-wave ODMR setup and subsequently use this pre-trained network on the sensor device to deduce the magnitude of the magnetic field from recorded ODMR spectra. In our proposed sensor setup, a low-cost and low-power ESP32 microcontroller development board is employed to control data recording and perform inference of the network. In a proof-of-concept study, we show that the setup is capable of measuring magnetic fields with high precision and has the potential to enable robust and accessible sensor applications with a wide measuring range.

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Machine Learning at the Edge

Cited by 12 publications

References 48 publications

Parameter estimation from quantum-jump data using neural networks

Parameter estimation from quantum-jump data using neural networks

Advancements in Artificial Intelligence Circuits and Systems (AICAS)

Edge-Machine-Learning-Assisted Robust Magnetometer Based on Randomly Oriented NV-Ensembles in Diamond

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