Real-Time Inference of Neural Networks on FPGAs for Motor Control Applications

Schindler, Tobias; Dietz, Armin

doi:10.1109/edpc51184.2020.9388185

Cited by 11 publications

(19 citation statements)

References 14 publications

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“…Although ML models are typically computationally more demanding than expert-driven models (e.g., LPTNs) the underlying linear algebra of ML model inference is particular suitable for fast execution on specialized hardware such as FPGAs [119] or microcontrollers with auxiliary ML cores [120]. Although such specialized hardware might be rather expensive today, more and more ML-related embedded control hardware products enter the market potentially leading to lower prices in the future.…”

Section: Machine Learning and Embedded Control Hardwarementioning

confidence: 99%

Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges

Wallscheid

2021

IEEE Open J. Ind. Applicat.

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Temperature sensing in electric motors is an important task to ensure component protection against excessive heat while maximizing the power and torque capabilities. In order to optimize the dynamic performance limits of a motor during online operation, important motor temperatures must be known in real time. Since temperature measurements are associated with costs and integration efforts, model-based estimation methods became highly relevant. In recent years, many promising contributions have been made to this field leading to a vast literature basis. This paper organizes the literary landscape and gives a bird's-eye overview of the three most important estimation classes: Indirect methods, which track temperature-sensitive electrical motor parameters, and direct methods, namely lumped-parameter thermal networks as well as supervised machine learning. Practical limitations as well as special challenges of the respective methods are emphasized throughout the text. The aim of the paper is therefore to facilitate the entry for interested researchers and engineers in this field as well as to stimulate new research impulses. Hence, open problems as well as future research questions are addressed at the end of this review.

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Section: Machine Learning and Embedded Control Hardwarementioning

confidence: 99%

Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges

Wallscheid

2021

IEEE Open J. Ind. Applicat.

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“…Although various artificial intelligence-based electric machine drives have been successfully implemented in embedded systems with digital signal processors (DSP) [83], [93], [102], [211], [235] or field-programmable gate arrays (FPGA) [40], [199], [236]- [241] during the past 30 years, most of them have rather shallow network structures and slow PWM cycles in the order of milliseconds. With the deployment of more advanced machine learning and deep learning algorithms to industrial applications such as electric machine drives, however, the inference of deep neural networks in real time, typically in the order of microseconds, is becoming a major challenge [242].…”

Section: Implementing Artificial Intelligence-based Motor Drives In Embedded Systemsmentioning

confidence: 99%

“…However, it should be noted that the 3 ms and 50 ms of latency are only examples of an autonomous vehicle application, whereas Ref. [242] showed the latency of a reinforcement learning-based motor control application can be reduced to as low as 7.36 µs on FPGAs, which is sufficient for a control frequency of 100 kHz. Specifically, the deployed neural network has 9,224 variables and the inference is performed using 32 DSP-slices, which are offered by the programmable logic part of the Xilinx FPGA to efficiently implement multiplications and multiply-accumulate operations.…”

Section: Implementing Artificial Intelligence-based Motor Drives In Embedded Systemsmentioning

confidence: 99%

“…Although DSP-slices are a limited resource on FPGAs, it seems there's still big head room for FPGAs to run inference on deeper and larger neural networks for motor controls. For example, the current implementation in [242] uses 32 DSP-slices to get to a point where the latency is below 10 µs, while the commonly-used Xilinx Zynq-7020 offers 220 DSP-slices [245], and Xilinx UltraScale ZU2EG offers 240 DSP-slices [246]. 2) High throughput: Based on the tightly-coupled systemon-chip (SoC) architecture, FPGAs can deliver a high throughput by optimizing hardware acceleration of AI inference in the programmable logic (PL) part and other performance-critical functions in the processing system (PS).…”

Section: Implementing Artificial Intelligence-based Motor Drives In Embedded Systemsmentioning

confidence: 99%

“…14. In [242], VHDL code for two multi-layer perception (MLP) neural networks is also generated by the HDL Coder.…”

Section: B Matlab Hdl Coder and Xilinx System Generator (Xsg)mentioning

confidence: 99%

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Artificial Intelligence in Electric Machine Drives: Advances and Trends

Zhang¹

2021

Preprint

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This review paper systematically summarizes the existing literature on applying classical AI techniques and advanced deep learning algorithms to electric machine drives. It is anticipated that with the rapid progress in deep learning models and embedded hardware platforms, AI-based data-driven approaches will become increasingly popular for the automated high-performance control of electric machines. Additionally, this paper also provides some outlook towards promoting its widespread application in the industry, such as implementing advanced RL algorithms with good domain adaptation and transfer learning capabilities and deploying them onto low-cost SoC FPGA devices.

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Robust stabilization of polytopic systems via fast and reliable neural network‐based approximations

Fabiani,

Goulart

2024

Intl J Robust & Nonlinear

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We consider the design of fast and reliable neural network‐based approximations of traditional stabilizing controllers for linear systems with polytopic uncertainty, including control laws with variable structure and those based on a (minimal) selection policy. Building upon recent approaches for the design of reliable control surrogates with guaranteed structural properties, we develop a systematic procedure to certify the closed‐loop stability and performance of a linear uncertain system when a trained rectified linear unit (ReLU)‐based approximation replaces such traditional controllers. First, we provide a sufficient condition, which involves the worst‐case approximation error between ReLU‐based and traditional controller‐based state‐to‐input mappings, ensuring that the system is ultimately bounded within a set with adjustable size and convergence rate. Then, we develop an offline, mixed‐integer optimization‐based method that allows us to compute that quantity exactly.

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Real-Time Inference of Neural Networks on FPGAs for Motor Control Applications

Cited by 11 publications

References 14 publications

Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges

Thermal Monitoring of Electric Motors: State-of-the-Art Review and Future Challenges

Artificial Intelligence in Electric Machine Drives: Advances and Trends

Robust stabilization of polytopic systems via fast and reliable neural network‐based approximations

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