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Found 9 papers, 4 papers with code
Scaling Limits of Memristor-Based Routers for Asynchronous Neuromorphic Systems
Multi-core neuromorphic systems typically use on-chip routers to transmit spikes among cores.
Neuromorphic analog circuits for robust on-chip always-on learning in spiking neural networks
We designed a spiking neural network with these learning circuits in a prototype chip using a 180 nm CMOS technology.
Synaptic metaplasticity with multi-level memristive devices
In this work, we extend this solution to quantized neural networks (QNNs) and present a memristor-based hardware solution for implementing metaplasticity during both inference and training.
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.
Online Training of Spiking Recurrent Neural Networks with Phase-Change Memory Synapses
These challenges are further accentuated, if one resorts to using memristive devices for in-memory computing to resolve the von-Neumann bottleneck problem, at the expense of a substantial increase in variability in both the computation and the working memory of the spiking RNNs.
On-Chip Error-triggered Learning of Multi-layer Memristive Spiking Neural Networks
Recent breakthroughs in neuromorphic computing show that local forms of gradient descent learning are compatible with Spiking Neural Networks (SNNs) and synaptic plasticity.
Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications
The advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors has brought on new opportunities for applying both Deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge.
Ultra-Low-Power FDSOI Neural Circuits for Extreme-Edge Neuromorphic Intelligence
Recent years have seen an increasing interest in the development of artificial intelligence circuits and systems for edge computing applications.
Sensor fusion using EMG and vision for hand gesture classification in mobile applications
The discrimination of human gestures using wearable solutions is extremely important as a supporting technique for assisted living, healthcare of the elderly and neurorehabilitation.
