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Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing
Despite a well-established mathematical foundation for neural dynamics, the implementation details vary greatly across different platforms.
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.
SuperNeuro: A Fast and Scalable Simulator for Neuromorphic Computing
Currently available simulators are catered to either neuroscience workflows (such as NEST and Brian2) or deep learning workflows (such as BindsNET).
Fully Non-Linear Neuromorphic Computing with Linear Wave Scattering
The key idea is to inject the input via physical parameters that affect the scattering processes.
Optics Emerging Technologies Data Analysis, Statistics and Probability
Scalable Optical Learning Operator
Today's heavy machine learning tasks are fueled by large datasets.
Introducing Astrocytes on a Neuromorphic Processor: Synchronization, Local Plasticity and Edge of Chaos
While there is still a lot to learn about astrocytes and their neuromodulatory role in the spatial and temporal integration of neuronal activity, their introduction to neuromorphic hardware is timely, facilitating their computational exploration in basic science questions as well as their exploitation in real-world applications.
Collective dynamics and long-range order in thermal neuristor networks
In the pursuit of scalable and energy-efficient neuromorphic devices, recent research has unveiled a novel category of spiking oscillators, termed ``thermal neuristors."
Disordered Systems and Neural Networks Materials Science Adaptation and Self-Organizing Systems
Shenjing: A low power reconfigurable neuromorphic accelerator with partial-sum and spike networks-on-chip
We show that conventional artificial neural networks (ANN) such as multilayer perceptron, convolutional neural networks, as well as the latest residual neural networks can be mapped successfully onto Shenjing, realizing ANNs with SNN's energy efficiency.
SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence
Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency by introducing neural dynamics and spike properties.
Single Headed Attention RNN: Stop Thinking With Your Head
The leading approaches in language modeling are all obsessed with TV shows of my youth - namely Transformers and Sesame Street.
Ranked #27 on
Language Modelling
on enwik8
