Search Results for author:
Found 10 papers, 8 papers with code
Low-power Ship Detection in Satellite Images Using Neuromorphic Hardware
For maritime ship detection, on-board data processing can identify ships and reduce the amount of data sent to the ground.
Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing
By abstracting away assumptions around discretization and hardware constraints, NIR faithfully captures the computational model, while bridging differences between the evaluated implementation and the underlying mathematical formalism.
Ultra-low-power Image Classification on Neuromorphic Hardware
We propose a temporal ANN-to-SNN conversion method, which we call Quartz, that is based on the time to first spike (TTFS).
NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
To address these shortcomings, we present NeuroBench: a benchmark framework for neuromorphic computing algorithms and systems.
EXODUS: Stable and Efficient Training of Spiking Neural Networks
In this paper, (i) we modify SLAYER and design an algorithm called EXODUS, that accounts for the neuron reset mechanism and applies the Implicit Function Theorem (IFT) to calculate the correct gradients (equivalent to those computed by BPTT), (ii) we eliminate the need for ad-hoc scaling of gradients, thus, reducing the training complexity tremendously, (iii) we demonstrate, via computer simulations, that EXODUS is numerically stable and achieves a comparable or better performance than SLAYER especially in various tasks with SNNs that rely on temporal features.
A Framework for Event-based Computer Vision on a Mobile Device
We present the first publicly available Android framework to stream data from an event camera directly to a mobile phone.
A sampling-based approach for efficient clustering in large datasets
We propose a simple and efficient clustering method for high-dimensional data with a large number of clusters.
Adversarial Attacks on Spiking Convolutional Neural Networks for Event-based Vision
Event-based dynamic vision sensors provide very sparse output in the form of spikes, which makes them suitable for low-power applications.
Training Spiking Neural Networks Using Lessons From Deep Learning
This paper serves as a tutorial and perspective showing how to apply the lessons learnt from several decades of research in deep learning, gradient descent, backpropagation and neuroscience to biologically plausible spiking neural neural networks.
Event-based Face Detection and Tracking in the Blink of an Eye
We will rely on a new feature that has never been used for such a task that relies on detecting eye blinks.
