Alpha Renner

Last Name

Renner

First Name

Alpha

ORCID

0000-0002-0724-4169

Show all metadata

Search Results

Publications 1 - 10 of 21

Towards neuromorphic control: A spiking neural network based PID controller for UAV
Stagsted, Rasmus; Vitale, Antonio; Binz, Jonas; et al. (2020)
Proceedings of Robotics: Science and Systems XVI
In this work, we present a spiking neural network (SNN) based PID controller on a neuromorphic chip. On-chip SNNs are currently being explored in low-power AI applications. Due to potentially ultra-low power consumption, low latency, and high processing speed, on-chip SNNs are a promising tool for control of power-constrained platforms, such as Unmanned Aerial Vehicles (UAV). To obtain highly efficient and fast end-to-end neuromorphic controllers, the SNN-based AI architectures must be seamlessly integrated with motor control. Towards this goal, we present here the first implementation of a fully neuromorphic PID controller. We interfaced Intel's neuromorphic research chip Loihi to a UAV, constrained to a single degree of freedom. We developed an SNN control architecture using populations of spiking neurons, in which each spike carries information about the measured, control, or error value, defined by the identity of the spiking neuron. Using this sparse code, we realize a precise PID controller. The P, I, and D gains of the controller are implemented as synaptic weights that can adapt according to an on-chip plasticity rule. In future work, these plastic synapses can be used to tune and adapt the controller autonomously.
Self-calibration and learning on chip: towards neuromorphic robots
Kreiser, Raphaela; Waibel, Gabriel; Renner, Alpha; et al. (2019)
Neuromorphic visual scene understanding with resonator networks
Renner, Alpha; Supic, Lazar; Danielescu, Andreea; et al. (2024)
Nature Machine Intelligence
Analysing a visual scene by inferring the configuration of a generative model is widely considered the most flexible and generalizable approach to scene understanding. Yet, one major problem is the computational challenge of the inference procedure, involving a combinatorial search across object identities and poses. Here we propose a neuromorphic solution exploiting three key concepts: (1) a computational framework based on vector symbolic architectures (VSAs) with complex-valued vectors, (2) the design of hierarchical resonator networks to factorize the non-commutative transforms translation and rotation in visual scenes and (3) the design of a multi-compartment spiking phasor neuron model for implementing complex-valued resonator networks on neuromorphic hardware. The VSA framework uses vector binding operations to form a generative image model in which binding acts as the equivariant operation for geometric transformations. A scene can therefore be described as a sum of vector products, which can then be efficiently factorized by a resonator network to infer objects and their poses. The hierarchical resonator network features a partitioned architecture in which vector binding is equivariant for horizontal and vertical translation within one partition and for rotation and scaling within the other partition. The spiking neuron model allows mapping the resonator network onto efficient and low-power neuromorphic hardware. Our approach is demonstrated on synthetic scenes composed of simple two-dimensional shapes undergoing rigid geometric transformations and colour changes. A companion paper demonstrates the same approach in real-world application scenarios for machine vision and robotics.
Visual Pattern Recognition with on On-Chip Learning: Towards a Fully Neuromorphic Approach
Baumgartner, Sandro; Renner, Alpha; Kreiser, Raphaela; et al. (2020)
2020 IEEE International Symposium on Circuits and Systems (ISCAS)
We present a spiking neural network (SNN) for visual pattern recognition with on-chip learning on neuromorphic hardware. We show how this network can learn simple visual patterns composed of horizontal and vertical bars sensed by a Dynamic Vision Sensor, using a local spike-based plasticity rule. During recognition, the network classifies the pattern's identity while at the same time estimating its location and scale. We build on previous work that used learning with neuromorphic hardware in the loop and demonstrate that the proposed network can properly operate with on-chip learning, demonstrating a complete neuromorphic pattern learning and recognition setup. Our results show that the network is robust against noise on the input (no accuracy drop when adding 130% noise) and against up to 20% noise in the neuron parameters.
Event-driven Vision and Control for UAVs on a Neuromorphic Chip
Vitale, Antonio; Renner, Alpha; Nauer, Celine; et al. (2021)
2021 IEEE International Conference on Robotics and Automation (ICRA)
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. Event-based 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.
Neuromorphic Visual Scene Understanding with Resonator Networks
Renner, Alpha; Supic, Lazar; Danielescu, Andreea; et al. (2022)
arXiv
Inferring the position of objects and their rigid transformations is still an open problem in visual scene understanding. Here we propose a neuromorphic solution that utilizes an efficient factorization network based on three key concepts: (1) a computational framework based on Vector Symbolic Architectures (VSA) with complex-valued vectors; (2) the design of Hierarchical Resonator Networks (HRN) to deal with the non-commutative nature of translation and rotation in visual scenes, when both are used in combination; (3) the design of a multi-compartment spiking phasor neuron model for implementing complex-valued vector binding on neuromorphic hardware. The VSA framework uses vector binding operations to produce generative image models in which binding acts as the equivariant operation for geometric transformations. A scene can therefore be described as a sum of vector products, which in turn can be efficiently factorized by a resonator network to infer objects and their poses. The HRN enables the definition of a partitioned architecture in which vector binding is equivariant for horizontal and vertical translation within one partition and for rotation and scaling within the other partition. The spiking neuron model allows mapping the resonator network onto efficient and low-power neuromorphic hardware. In this work, we demonstrate our approach using synthetic scenes composed of simple 2D shapes undergoing rigid geometric transformations and color changes. A companion paper demonstrates this approach in real-world application scenarios for machine vision and robotics.
Implementing Backpropagation for Learning on Neuromorphic Spiking Hardware
Renner, Alpha; Sheldon, Forrest; Zlotnik, Anatoly; et al. (2020)
Proceedings of the Neur-Inspired Computational Elements Workshop (NICE '20)
Many contemporary advances in the theory and practice of neural networks are inspired by our understanding of how information is processed by natural neural systems. However, the basis of modern deep neural networks remains the error backpropagation algorithm [1], which though founded in rigorous mathematical optimization theory, has not been successfully demonstrated in a neurophysiologically realistic circuit. In a recent study, we proposed a neuromorphic architecture for learning that tunes the propagation of information forward and backwards through network layers using an endogenous timing mechanism controlled by thresholding of intensities [2]. This mechanism was demonstrated in simulation of analog currents, which represent the mean fields of spiking neuron populations. In this follow-on study, we present a modified architecture that includes several new mechanisms that enable implementation of the backpropagation algorithm using neuromorphic spiking units.We demonstrate the function of this architecture in learning mapping examples, both in event-based simulation as well as a true hardware implementation. © 2020 ACM.
Accurate Mapping of RNNs on Neuromorphic Hardware with Adaptive Spiking Neurons
Boeshertz, Gauthier; Indiveri, Giacomo; Nair, Manu; et al. (2024)
2024 International Conference on Neuromorphic Systems (ICONS)
Thanks to their parallel and sparse activity features, recurrent neural networks (RNNs) are well-suited for hardware implementation in low-power neuromorphic hardware. However, mapping rate-based RNNs to hardware-compatible spiking neural networks (SNNs) remains challenging. Here, we present a ΣΔ−low-pass RNN (lpRNN): an RNN architecture employing an adaptive spiking neuron model that encodes signals using ΣΔ-modulation and enables precise mapping. The ΣΔ-neuron communicates graded values using spike timing, and the dynamics of the IpRNN are set to match typical timescales for processing natural signals, such as speech. Our approach integrates rate and temporal coding, offering a robust solution for efficient and accurate conversion of RNNs to SNNs. We demonstrate the implementation of the IpRNN on Intel's neuromorphic research chip Loihi, achieving state-of-the-art classification results on audio benchmarks using 3-bit weights. These results call for a deeper investigation of recurrency and adaptation in event-based systems, which may lead to insights for edge computing applications where power-efficient real-time inference is required.
Distributed representations enable robust multi-timescale symbolic computation in neuromorphic hardware
Cotteret, Madison; Greatorex, Hugh; Renner, Alpha; et al. (2025)
Neuromorphic Computing and Engineering
Programming recurrent spiking neural networks (RSNNs) to robustly perform multi-timescale computation remains a difficult challenge. To address this, we describe a single-shot weight learning scheme to embed robust multi-timescale dynamics into attractor-based RSNNs, by exploiting the properties of high-dimensional distributed representations. We embed finite state machines into the RSNN dynamics by superimposing a symmetric autoassociative weight matrix and asymmetric transition terms, which are each formed by the vector binding of an input and heteroassociative outer-products between states. Our approach is validated through simulations with highly nonideal weights; an experimental closed-loop memristive hardware setup; and on Loihi 2, where it scales seamlessly to large state machines. This work introduces a scalable approach to embed robust symbolic computation through recurrent dynamics into neuromorphic hardware, without requiring parameter fine-tuning or significant platform-specific optimisation. Moreover, it demonstrates that distributed symbolic representations serve as a highly capable representation-invariant language for cognitive algorithms in neuromorphic hardware.
Neuromorphic Visual Scene Understanding with Resonator Networks (in brief)
Renner, Alpha; Indiveri, Giacomo; Supic, Lazar; et al. (2022)
NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations
Inferring the position of objects and their rigid transformations is still an open problem in visual scene understanding. Here we propose a neuromorphic framework that poses scene understanding as a factorization problem and uses a resonator network to extract object identities and their transformations. The framework uses vector binding operations to produce generative image models in which binding acts as the equivariant operation for geometric transformations. A scene can therefore be described as a sum of vector products, which in turn can be efficiently factorized by a resonator network to infer objects and their poses. We also describe a hierarchical resonator network that enables the definition of a partitioned architecture in which vector binding is equivariant for horizontal and vertical translation within one partition, and for rotation and scaling within the other partition. We demonstrate our approach using synthetic scenes composed of simple 2D shapes undergoing rigid geometric transformations and color changes.

Publications 1 - 10 of 21

Alpha Renner

Last Name

First Name

ORCID

Organisational unit

Filters

Search Results