Closed-Loop Spiking Control on a Neuromorphic Processor Implemented on the iCub

Zhao, Jingyue; Risi, Nicoletta; Monforte, Marco; Bartolozzi, Chiara; Indiveri, Giacomo; Donati, Elisa

doi:10.1109/jetcas.2020.3040390

Cited by 20 publications

(11 citation statements)

References 22 publications

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“…These approaches are often hand-tooled for a given neuromorphic implementation, and it is not clear what the appropriate central pattern generator would be for each individual task. There have been multiple neuromorphic solutions for designing the PID control [52,62] or proportional integral control system [19,61]. Again, these are often hand-tooled for a given solution.…”

Section: Background and Related Workmentioning

confidence: 99%

Evolutionary vs imitation learning for neuromorphic control at the edge*

Schuman

Patton

Kulkarni

et al. 2022

Neuromorph. Comput. Eng.

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Neuromorphic computing offers the opportunity to implement extremely low power artificial intelligence at the edge. Control applications, such as autonomous vehicles and robotics, are also of great interest for neuromorphic systems at the edge. It is not clear, however, what the best neuromorphic training approaches are for control applications at the edge. In this work, we implement and compare the performance of evolutionary optimization and imitation learning approaches on an autonomous race car control task using an edge neuromorphic implementation. We show that the evolutionary approaches tend to achieve better performing smaller network sizes that are well-suited to edge deployment, but they also take significantly longer to train. We also describe a workflow to allow for future algorithmic comparisons for neuromorphic hardware on control applications at the edge.

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Section: Background and Related Workmentioning

confidence: 99%

Evolutionary vs imitation learning for neuromorphic control at the edge*

Schuman

Patton

Kulkarni

et al. 2022

Neuromorph. Comput. Eng.

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“…In our previous work, we have introduced both SNNbased proportional (P) controllers as well as proportional, integral, derivative (PID) controllers, all implemented in neuromorphic hardware [24]- [26]. This work inspired a number of recent neuromorphic motor control architectures [27] and continued the long-standing research line of neuronally inspired motor control methods [28], [29].…”

Section: Introductionmentioning

confidence: 99%

Event-driven Vision and Control for UAVs on a Neuromorphic Chip

Vitale

Renner

Nauer

et al. 2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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Event-based vision sensors achieve up to three orders of magnitude better speed vs. power consumption trade off in high-speed control of UAVs compared to conventional image sensors. Eventbased cameras produce a sparse stream of events that can be processed more efficiently and with a lower latency than images, enabling ultra-fast vision-driven control. Here, we explore how an event-based vision algorithm can be implemented as a spiking neuronal network on a neuromorphic chip and used in a drone controller. We show how seamless integration of event-based perception on chip leads to even faster control rates and lower latency. In addition, we demonstrate how online adaptation of the SNN controller can be realised using on-chip learning. Our spiking neuronal network on chip is the first example of a neuromorphic vision-based controller on chip solving a high-speed UAV control task. The excellent scalability of processing in neuromorphic hardware opens the possibility to solve more challenging visual tasks in the future and integrate visual perception in fast control loops.

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“…As such, they represent a promising alternative to traditional deep learning approaches for edge applications that require ultra-low power computing capabilities. Recently, SNNs have been used to solve a wide range of engineering problems, such as image classification speech recognition [2], sensor fusion [3], motor control [4], biomedical signal processing [5] and vibration-based soundlocalization [6].…”

Section: Introductionmentioning

confidence: 99%

Online Detection of Vibration Anomalies Using Balanced Spiking Neural Networks

Dennler

Haessig

Cartiglia

et al. 2021

2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS)

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Vibration patterns yield valuable information about the health state of a running machine, which is commonly exploited in predictive maintenance tasks for large industrial systems. However, the overhead, in terms of size, complexity and power budget, required by classical methods to exploit this information is often prohibitive for smaller-scale applications such as autonomous cars, drones or robotics. Here we propose a neuromorphic approach to perform vibration analysis using spiking neural networks that can be applied to a wide range of scenarios. We present a spike-based end-to-end pipeline able to detect system anomalies from vibration data, using building blocks that are compatible with analog-digital neuromorphic circuits. This pipeline operates in an online unsupervised fashion, and relies on a cochlea model, on feedback adaptation and on a balanced spiking neural network. We show that the proposed method achieves state-of-the-art performance or better against two publicly available data sets. Further, we demonstrate a working proof-of-concept implemented on an asynchronous neuromorphic processor device. This work represents a significant step towards the design and implementation of autonomous lowpower edge-computing devices for online vibration monitoring.

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Closed-Loop Spiking Control on a Neuromorphic Processor Implemented on the iCub

Cited by 20 publications

References 22 publications

Evolutionary vs imitation learning for neuromorphic control at the edge*

Evolutionary vs imitation learning for neuromorphic control at the edge*

Event-driven Vision and Control for UAVs on a Neuromorphic Chip

Online Detection of Vibration Anomalies Using Balanced Spiking Neural Networks

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