arXiv subjects

André van Schaik

Publications and source records attributed to André van Schaik.

At least 19 recordsLinked to original sources

arXiv2026

Programmable Silicon Retina on Pixel Processor Array

Standard dynamic vision sensors approximate retinal processing by detecting temporal contrast changes, offering high speed and high dynamic range. In this work, we explore whether incorporating additional biologically inspired processing stages - specifically spatial filtering and gain control - can offer advantages for certain downstream tasks such as saliency prediction. We present the first implementation of a multi-stage Silicon Retina model on the SCAMP-5 Pixel Processor Array, along with a GPU-based simulation framework. We evaluate the performance of our model on Video Intensity Reconstruction and Video Saliency Prediction. While the bio-inspired model is less effective at reconstructing absolute intensity frames, it achieves a 13\% reduction in saliency prediction loss in comparison to standard DVS event representation, while reducing the event rate by approximately 47\%. These experiments are obtained using a lightweight $\approx 100$k-parameter FireNet-style network, adapted from event-based reconstruction to saliency prediction. These results suggest that the silicon retina's "information distillation" mechanism can achieve a more efficient representation for downstream neural networks, particularly in bandwidth-constrained edge applications.

eess.IV↗

arXiv2026

Odour sensing in turbulent plumes with high-speed electronic nose and non-invasive ground truth

Chemical sensing in real-world environments requires resolving rapidly fluctuating and spatially heterogeneous concentration fields. However, these dynamics are strongly distorted by widely used, low-cost metal-oxide (MOx) gas sensors, whose thermal and surface-kinetic response acts as a low-pass filter on the underlying concentration signal. Quantifying and compensating for these effects remains challenging, largely due to the lack of benchmark datasets that simultaneously capture the spatiotemporal structure of turbulent odour fields and the time-resolved response of point sensors. Here, we present a dataset combining planar laser-induced fluorescence (PLIF) measurements of an acetone tracer plume with synchronised recordings from a custom, kilohertz-rate microelectromechanical (MEMS) MOx electronic nose deployed in a laboratory wind tunnel. The PLIF system provides quantitative, two-dimensional concentration fields at high spatial and temporal resolution, while the co-located e-nose records film resistance, heater currents, and environmental parameters with aligned timestamps. The dataset enables quantitative assessment of sensor dynamics, development and benchmarking of reconstruction and deconvolution algorithms, and data-driven modelling of plume structure. All recordings, metadata, calibration files, and example analysis scripts are released in open, platform-independent formats. Together, these provide a valuable reference for researchers working in odour-guided robotics, environmental monitoring, computational fluid dynamics, and neuromorphic sensing, supporting the design and evaluation of high-speed odour-sensing systems.

eess.SP↗

arXiv2025

Neural Signal Compression using RAMAN tinyML Accelerator for BCI Applications

High-quality, multi-channel neural recording is indispensable for neuroscience research and clinical applications. Large-scale brain recordings often produce vast amounts of data that must be wirelessly transmitted for subsequent offline analysis and decoding, especially in brain-computer interfaces (BCIs) utilizing high-density intracortical recordings with hundreds or thousands of electrodes. However, transmitting raw neural data presents significant challenges due to limited communication bandwidth and resultant excessive heating. To address this challenge, we propose a neural signal compression scheme utilizing Convolutional Autoencoders (CAEs), which achieves a compression ratio of up to 150 for compressing local field potentials (LFPs). The CAE encoder section is implemented on RAMAN, an energy-efficient tinyML accelerator designed for edge computing. RAMAN leverages sparsity in activation and weights through zero skipping, gating, and weight compression techniques. Additionally, we employ hardware-software co-optimization by pruning the CAE encoder model parameters using a hardware-aware balanced stochastic pruning strategy, resolving workload imbalance issues and eliminating indexing overhead to reduce parameter storage requirements by up to 32.4%. Post layout simulation shows that the RAMAN encoder can be implemented in a TSMC 65-nm CMOS process, occupying a core area of 0.0187 mm2 per channel. Operating at a clock frequency of 2 MHz and a supply voltage of 1.2 V, the estimated power consumption is 15.1 uW per channel for the proposed DS-CAE1 model. For functional validation, the RAMAN encoder was also deployed on an Efinix Ti60 FPGA, utilizing 37.3k LUTs and 8.6k flip-flops. The compressed neural data from RAMAN is reconstructed offline with SNDR of 22.6 dB and 27.4 dB, along with R2 scores of 0.81 and 0.94, respectively, evaluated on two monkey neural recordings.

cs.AR↗

arXiv2025

A Neuromorphic Electronic Nose Design

Rapid detection of gas concentration is important in different domains like gas leakage monitoring, pollution control, and so on, for the prevention of health hazards. Out of different types of gas sensors, Metal oxide (MOx) sensors are extensively used in such applications because of their portability, low cost, and high sensitivity for specific gases. However, how to effectively sample the MOx data for the real-time detection of gas and its concentration level remains an open question. Here, we introduce a simple analog front-end for one MOx sensor that encodes the gas concentration in the time difference between pulses of two separate pathways. This front-end design is inspired by the spiking output of a mammalian olfactory bulb. We show that for a gas pulse injected in a constant airflow, the time difference between pulses decreases with increasing gas concentration, similar to the spike time difference between the two principal output neurons in the olfactory bulb. The circuit design is further extended to a MOx sensor array, and this sensor array front-end was tested in the same environment for gas identification and concentration estimation. Encoding of gas stimulus features in analog spikes at the sensor level itself may result in data and power-efficient real-time gas sensing systems in the future that can ultimately be used in uncontrolled and turbulent environments for longer periods without data explosion.

cs.NE↗

arXiv2025

NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accurately measure technological advancements, compare performance with conventional methods, and identify promising future research directions. Prior neuromorphic computing benchmark efforts have not seen widespread adoption due to a lack of inclusive, actionable, and iterative benchmark design and guidelines. To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems. NeuroBench is a collaboratively-designed effort from an open community of researchers across industry and academia, aiming to provide a representative structure for standardizing the evaluation of neuromorphic approaches. The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings. In this article, we outline tasks and guidelines for benchmarks across multiple application domains, and present initial performance baselines across neuromorphic and conventional approaches for both benchmark tracks. NeuroBench is intended to continually expand its benchmarks and features to foster and track the progress made by the research community.

cs.AI↗

arXiv2024

Neuromorphic circuit for temporal odor encoding in turbulent environments

Natural odor environments present turbulent and dynamic conditions, causing chemical signals to fluctuate in space, time, and intensity. While many species have evolved highly adaptive behavioral responses to such variability, the emerging field of neuromorphic olfaction continues to grapple with the challenge of efficiently sampling and identifying odors in real-time. In this work, we investigate Metal-Oxide (MOx) gas sensor recordings of constant airflow-embedded artificial odor plumes. We discover a data feature that is representative of the presented odor stimulus at a certain concentration - irrespective of temporal variations caused by the plume dynamics. Further, we design a neuromorphic electronic nose front-end circuit for extracting and encoding this feature into analog spikes for gas detection and concentration estimation. The design is inspired by the spiking output of parallel neural pathways in the mammalian olfactory bulb. We test the circuit for gas recognition and concentration estimation in artificial environments, where either single gas pulses or pre-recorded odor plumes were deployed in a constant flow of air. For both environments, our results indicate that the gas concentration is encoded in -- and inversely proportional to the time difference of analog spikes emerging out of two parallel pathways, similar to the spiking output of a mammalian olfactory bulb. The resulting neuromorphic nose could enable data-efficient, real-time robotic plume navigation systems, advancing the capabilities of odor source localization in applications such as environmental monitoring and search-and-rescue.

cs.NE↗

arXiv2024

High-speed odour sensing using miniaturised electronic nose

Animals have evolved to rapidly detect and recognise brief and intermittent encounters with odour packages, exhibiting recognition capabilities within milliseconds. Artificial olfaction has faced challenges in achieving comparable results -- existing solutions are either slow; or bulky, expensive, and power-intensive -- limiting applicability in real-world scenarios for mobile robotics. Here we introduce a miniaturised high-speed electronic nose; characterised by high-bandwidth sensor readouts, tightly controlled sensing parameters and powerful algorithms. The system is evaluated on a high-fidelity odour delivery benchmark. We showcase successful classification of tens-of-millisecond odour pulses, and demonstrate temporal pattern encoding of stimuli switching with up to 60 Hz. Those timescales are unprecedented in miniaturised low-power settings, and demonstrably exceed the performance observed in mice. For the first time, it is possible to match the temporal resolution of animal olfaction in robotic systems. This will allow for addressing challenges in environmental and industrial monitoring, security, neuroscience, and beyond.

eess.SP↗

arXiv2023

An Event based Prediction Suffix Tree

This article introduces the Event based Prediction Suffix Tree (EPST), a biologically inspired, event-based prediction algorithm. The EPST learns a model online based on the statistics of an event based input and can make predictions over multiple overlapping patterns. The EPST uses a representation specific to event based data, defined as a portion of the power set of event subsequences within a short context window. It is explainable, and possesses many promising properties such as fault tolerance, resistance to event noise, as well as the capability for one-shot learning. The computational features of the EPST are examined in a synthetic data prediction task with additive event noise, event jitter, and dropout. The resulting algorithm outputs predicted projections for the near term future of the signal, which may be applied to tasks such as event based anomaly detection or pattern recognition.

cs.LG↗

arXiv2023

Spike-time encoding of gas concentrations using neuromorphic analog sensory front-end

Gas concentration detection is important for applications such as gas leakage monitoring. Metal Oxide (MOx) sensors show high sensitivities for specific gases, which makes them particularly useful for such monitoring applications. However, how to efficiently sample and further process the sensor responses remains an open question. Here we propose a simple analog circuit design inspired by the spiking output of the mammalian olfactory bulb and by event-based vision sensors. Our circuit encodes the gas concentration in the time difference between the pulses of two separate pathways. We show that in the setting of controlled airflow-embedded gas injections, the time difference between the two generated pulses varies inversely with gas concentration, which is in agreement with the spike timing difference between tufted cells and mitral cells of the mammalian olfactory bulb. Encoding concentration information in analog spike timings may pave the way for rapid and efficient gas detection, and ultimately lead to data- and power-efficient monitoring devices to be deployed in uncontrolled and turbulent environments.

cs.NE↗

arXiv2023

Limitations in odour recognition and generalisation in a neuromorphic olfactory circuit

Neuromorphic computing is one of the few current approaches that have the potential to significantly reduce power consumption in Machine Learning and Artificial Intelligence. Imam & Cleland presented an odour-learning algorithm that runs on a neuromorphic architecture and is inspired by circuits described in the mammalian olfactory bulb. They assess the algorithm's performance in "rapid online learning and identification" of gaseous odorants and odorless gases (short "gases") using a set of gas sensor recordings of different odour presentations and corrupting them by impulse noise. We replicated parts of the study and discovered limitations that affect some of the conclusions drawn. First, the dataset used suffers from sensor drift and a non-randomised measurement protocol, rendering it of limited use for odour identification benchmarks. Second, we found that the model is restricted in its ability to generalise over repeated presentations of the same gas. We demonstrate that the task the study refers to can be solved with a simple hash table approach, matching or exceeding the reported results in accuracy and runtime. Therefore, a validation of the model that goes beyond restoring a learned data sample remains to be shown, in particular its suitability to odour identification tasks.

cs.NE↗

arXiv2023

RAMAN: A Re-configurable and Sparse tinyML Accelerator for Inference on Edge

Deep Neural Network (DNN) based inference at the edge is challenging as these compute and data-intensive algorithms need to be implemented at low cost and low power while meeting the latency constraints of the target applications. Sparsity, in both activations and weights inherent to DNNs, is a key knob to leverage. In this paper, we present RAMAN, a Re-configurable and spArse tinyML Accelerator for infereNce on edge, architected to exploit the sparsity to reduce area (storage), power as well as latency. RAMAN can be configured to support a wide range of DNN topologies - consisting of different convolution layer types and a range of layer parameters (feature-map size and the number of channels). RAMAN can also be configured to support accuracy vs power/latency tradeoffs using techniques deployed at compile-time and run-time. We present the salient features of the architecture, provide implementation results and compare the same with the state-of-the-art. RAMAN employs novel dataflow inspired by Gustavson's algorithm that has optimal input activation (IA) and output activation (OA) reuse to minimize memory access and the overall data movement cost. The dataflow allows RAMAN to locally reduce the partial sum (Psum) within a processing element array to eliminate the Psum writeback traffic. Additionally, we suggest a method to reduce peak activation memory by overlapping IA and OA on the same memory space, which can reduce storage requirements by up to 50%. RAMAN was implemented on a low-power and resource-constrained Efinix Ti60 FPGA with 37.2K LUTs and 8.6K register utilization. RAMAN processes all layers of the MobileNetV1 model at 98.47 GOp/s/W and the DS-CNN model at 79.68 GOp/s/W by leveraging both weight and activation sparsity.

cs.NE↗

arXiv2023

Efficient Implementation of a Multi-Layer Gradient-Free Online-Trainable Spiking Neural Network on FPGA

This paper presents an efficient hardware implementation of the recently proposed Optimized Deep Event-driven Spiking Neural Network Architecture (ODESA). ODESA is the first network to have end-to-end multi-layer online local supervised training without using gradients and has the combined adaptation of weights and thresholds in an efficient hierarchical structure. This research shows that the network architecture and the online training of weights and thresholds can be implemented efficiently on a large scale in hardware. The implementation consists of a multi-layer Spiking Neural Network (SNN) and individual training modules for each layer that enable online self-learning without using back-propagation. By using simple local adaptive selection thresholds, a Winner-Takes-All (WTA) constraint on each layer, and a modified weight update rule that is more amenable to hardware, the trainer module allocates neuronal resources optimally at each layer without having to pass high-precision error measurements across layers. All elements in the system, including the training module, interact using event-based binary spikes. The hardware-optimized implementation is shown to preserve the performance of the original algorithm across multiple spatial-temporal classification problems with significantly reduced hardware requirements.

cs.NE↗

arXiv2022

Event-driven Spectrotemporal Feature Extraction and Classification using a Silicon Cochlea Model

This paper presents a reconfigurable digital implementation of an event-based binaural cochlear system on a Field Programmable Gate Array (FPGA). It consists of a pair of the Cascade of Asymmetric Resonators with Fast Acting Compression (CAR FAC) cochlea models and leaky integrate and fire (LIF) neurons. Additionally, we propose an event-driven SpectroTemporal Receptive Field (STRF) Feature Extraction using Adaptive Selection Thresholds (FEAST). It is tested on the TIDIGTIS benchmark and compared with current event-based auditory signal processing approaches and neural networks.

eess.SP↗

arXiv2022

Astrometric Calibration and Source Characterisation of the Latest Generation Neuromorphic Event-based Cameras for Space Imaging

As an emerging approach to space situational awareness and space imaging, the practical use of an event-based camera in space imaging for precise source analysis is still in its infancy. The nature of event-based space imaging and data collection needs to be further explored to develop more effective event-based space image systems and advance the capabilities of event-based tracking systems with improved target measurement models. Moreover, for event measurements to be meaningful, a framework must be investigated for event-based camera calibration to project events from pixel array coordinates in the image plane to coordinates in a target resident space object's reference frame. In this paper, the traditional techniques of conventional astronomy are reconsidered to properly utilise the event-based camera for space imaging and space situational awareness. This paper presents the techniques and systems used for calibrating an event-based camera for reliable and accurate measurement acquisition. These techniques are vital in building event-based space imaging systems capable of real-world space situational awareness tasks. By calibrating sources detected using the event-based camera, the spatio-temporal characteristics of detected sources or `event sources' can be related to the photometric characteristics of the underlying astrophysical objects. Finally, these characteristics are analysed to establish a foundation for principled processing and observing techniques which appropriately exploit the capabilities of the event-based camera.

eess.SP↗

arXiv2021

Drift in a Popular Metal Oxide Sensor Dataset Reveals Limitations for Gas Classification Benchmarks

Metal oxide (MOx) electro-chemical gas sensors are a sensible choice for many applications, due to their tunable sensitivity, their space-efficiency and their low price. Publicly available sensor datasets streamline the development and evaluation of novel algorithm and circuit designs, making them particularly valuable for the Artificial Olfaction / Mobile Robot Olfaction community. In 2013, Vergara et al. published a dataset comprising 16 months of recordings from a large MOx gas sensor array in a wind tunnel, which has since become a standard benchmark in the field. Here we report a previously undetected property of the dataset that limits its suitability for gas classification studies. The analysis of individual measurement timestamps reveals that gases were recorded in temporally clustered batches. The consequential correlation between the sensor response before gas exposure and the time of recording is often sufficient to predict the gas used in a given trial. Even if compensated by zero-offset-subtraction, residual short-term drift contains enough information for gas classification. We have identified a minimally drift-affected subset of the data, which is suitable for gas classification benchmarking after zero-offset-subtraction, although gas classification performance was substantially lower than for the full dataset. We conclude that previous studies conducted with this dataset very likely overestimate the accuracy of gas classification results. For the 17 potentially affected publications, we urge the authors to re-evaluate the results in light of our findings. Our observations emphasize the need to thoroughly document gas sensing datasets, and proper validation before using them for the development of algorithms.

eess.SP↗

arXiv2021

Superevents: Towards Native Semantic Segmentation for Event-based Cameras

Most successful computer vision models transform low-level features, such as Gabor filter responses, into richer representations of intermediate or mid-level complexity for downstream visual tasks. These mid-level representations have not been explored for event cameras, although it is especially relevant to the visually sparse and often disjoint spatial information in the event stream. By making use of locally consistent intermediate representations, termed as superevents, numerous visual tasks ranging from semantic segmentation, visual tracking, depth estimation shall benefit. In essence, superevents are perceptually consistent local units that delineate parts of an object in a scene. Inspired by recent deep learning architectures, we present a novel method that employs lifetime augmentation for obtaining an event stream representation that is fed to a fully convolutional network to extract superevents. Our qualitative and quantitative experimental results on several sequences of a benchmark dataset highlights the significant potential for event-based downstream applications.

cs.CV↗

arXiv2020

Source localization using particle filtering on FPGA for robotic navigation with imprecise binary measurement

Particle filtering is a recursive Bayesian estimation technique that has gained popularity recently for tracking and localization applications. It uses Monte Carlo simulation and has proven to be a very reliable technique to model non-Gaussian and non-linear elements of physical systems. Particle filters outperform various other traditional filters like Kalman filters in non-Gaussian and non-linear settings due to their non-analytical and non-parametric nature. However, a significant drawback of particle filters is their computational complexity, which inhibits their use in real-time applications with conventional CPU or DSP based implementation schemes. This paper proposes a modification to the existing particle filter algorithm and presents a highspeed and dedicated hardware architecture. The architecture incorporates pipelining and parallelization in the design to reduce execution time considerably. The design is validated for a source localization problem wherein we estimate the position of a source in real-time using the particle filter algorithm implemented on hardware. The validation setup relies on an Unmanned Ground Vehicle (UGV) with a photodiode housing on top to sense and localize a light source. We have prototyped the design using Artix-7 field-programmable gate array (FPGA), and resource utilization for the proposed system is presented. Further, we show the execution time and estimation accuracy of the high-speed architecture and observe a significant reduction in computational time. Our implementation of particle filters on FPGA is scalable and modular, with a low execution time of about 5.62 us for processing 1024 particles and can be deployed for real-time applications.

cs.RO↗

arXiv2019

Event-based Feature Extraction Using Adaptive Selection Thresholds

Unsupervised feature extraction algorithms form one of the most important building blocks in machine learning systems. These algorithms are often adapted to the event-based domain to perform online learning in neuromorphic hardware. However, not designed for the purpose, such algorithms typically require significant simplification during implementation to meet hardware constraints, creating trade offs with performance. Furthermore, conventional feature extraction algorithms are not designed to generate useful intermediary signals which are valuable only in the context of neuromorphic hardware limitations. In this work a novel event-based feature extraction method is proposed that focuses on these issues. The algorithm operates via simple adaptive selection thresholds which allow a simpler implementation of network homeostasis than previous works by trading off a small amount of information loss in the form of missed events that fall outside the selection thresholds. The behavior of the selection thresholds and the output of the network as a whole are shown to provide uniquely useful signals indicating network weight convergence without the need to access network weights. A novel heuristic method for network size selection is proposed which makes use of noise events and their feature representations. The use of selection thresholds is shown to produce network activation patterns that predict classification accuracy allowing rapid evaluation and optimization of system parameters without the need to run back-end classifiers. The feature extraction method is tested on both the N-MNIST benchmarking dataset and a dataset of airplanes passing through the field of view. Multiple configurations with different classifiers are tested with the results quantifying the resultant performance gains at each processing stage.

cs.NE↗