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Found 13 papers, 4 papers with code
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.
Unleashing the Potential of Spiking Neural Networks by Dynamic Confidence
In this regard, Dynamic Confidence represents a meaningful step toward realizing the potential of SNNs.
Unleashing the Potential of Spiking Neural Networks with Dynamic Confidence
In this regard, Dynamic Confidence represents a meaningful step toward realizing the potential of SNNs.
An FPGA Implementation of Convolutional Spiking Neural Networks for Radioisotope Identification
This paper details the FPGA implementation methodology for Convolutional Spiking Neural Networks (CSNN) and applies this methodology to low-power radioisotope identification using high-resolution data.
Spiking Neural Network Based Low-Power Radioisotope Identification using FPGA
this paper presents a detailed methodology of a Spiking Neural Network (SNN) based low-power design for radioisotope identification.
Low-Power Low-Latency Keyword Spotting and Adaptive Control with a SpiNNaker 2 Prototype and Comparison with Loihi
We implemented two neural network based benchmark tasks on a prototype chip of the second-generation SpiNNaker (SpiNNaker 2) neuromorphic system: keyword spotting and adaptive robotic control.
Event-based Signal Processing for Radioisotope Identification
This paper identifies the problem of unnecessary high power overhead of the conventional frame-based radioisotope identification process and proposes an event-based signal processing process to address the problem established.
ATIS + SpiNNaker: a Fully Event-based Visual Tracking Demonstration
The Asynchronous Time-based Image Sensor (ATIS) and the Spiking Neural Network Architecture (SpiNNaker) are both neuromorphic technologies that "unconventionally" use binary spikes to represent information.
Dynamic Power Management for Neuromorphic Many-Core Systems
By measurement of three neuromorphic benchmarks it is shown that the total PE power consumption can be reduced by 75%, with 80% baseline power reduction and a 50% reduction of energy per neuron and synapse computation, all while maintaining temporary peak system performance to achieve biological real-time operation of the system.
Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 Prototype
Advances in neuroscience uncover the mechanisms employed by the brain to efficiently solve complex learning tasks with very limited resources.
Navigating the Landscape for Real-time Localisation and Mapping for Robotics and Virtual and Augmented Reality
Visual understanding of 3D environments in real-time, at low power, is a huge computational challenge.
Noisy Softplus: an activation function that enables SNNs to be trained as ANNs
We extended the work of proposed activation function, Noisy Softplus, to fit into training of layered up spiking neural networks (SNNs).
Introducing SLAMBench, a performance and accuracy benchmarking methodology for SLAM
Real-time dense computer vision and SLAM offer great potential for a new level of scene modelling, tracking and real environmental interaction for many types of robot, but their high computational requirements mean that use on mass market embedded platforms is challenging.
