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Found 22 papers, 5 papers with code
An Event based Prediction Suffix Tree
This article introduces the Event based Prediction Suffix Tree (EPST), a biologically inspired, event-based prediction algorithm.
Spike-time encoding of gas concentrations using neuromorphic analog sensory front-end
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.
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.
RAMAN: A Re-configurable and Sparse tinyML Accelerator for Inference on Edge
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.
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).
NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
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.
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).
Astrometric Calibration and Source Characterisation of the Latest Generation Neuromorphic Event-based Cameras for Space Imaging
These techniques are vital in building event-based space imaging systems capable of real-world space situational awareness tasks.
An Optimized Deep Spiking Neural Network Architecture Without Gradients
Through the use of simple local adaptive selection thresholds at each node, the network rapidly learns to appropriately allocate its neuronal resources at each layer for any given problem without using an error measure.
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.
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.
Event-based Feature Extraction Using Adaptive Selection Thresholds
A novel heuristic method for network size selection is proposed which makes use of noise events and their feature representations.
Large-Scale Neuromorphic Spiking Array Processors: A quest to mimic the brain
Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems.
EMNIST: an extension of MNIST to handwritten letters
The MNIST dataset has become a standard benchmark for learning, classification and computer vision systems.
Ranked #6 on
Image Classification
on EMNIST-Digits
A Stochastic Approach to STDP
The decay generator will then generate an exponential decay, which will be used by the STDP adaptor to perform the weight adaption.
A neuromorphic hardware framework based on population coding
These neuronal populations are characterised by a diverse distribution of tuning curves, ensuring that the entire range of input stimuli is encoded.
FPGA Implementation of the CAR Model of the Cochlea
Here, we implement the Cascade of Asymmetric Resonators (CAR) model of the cochlea on an FPGA.
Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the 'extreme learning machine' algorithm
Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems.
Learning ELM network weights using linear discriminant analysis
We present an alternative to the pseudo-inverse method for determining the hidden to output weight values for Extreme Learning Machines performing classification tasks.
Explicit Computation of Input Weights in Extreme Learning Machines
In the absence of supervised training for the input weights, random linear combinations of training data samples are used to project the input data to a higher dimensional hidden layer.
Online and Adaptive Pseudoinverse Solutions for ELM Weights
The ELM method has become widely used for classification and regressions problems as a result of its accuracy, simplicity and ease of use.
ELM Solutions for Event-Based Systems
The modifications involve the re-definition of hidden layer units as synaptic kernels, in which the input delta functions are transformed into continuous-valued signals using a variety of impulse-response functions.
