Search Results for author:
Found 17 papers, 6 papers with code
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).
The Intel Neuromorphic DNS Challenge
A critical enabler for progress in neuromorphic computing research is the ability to transparently evaluate different neuromorphic solutions on important tasks and to compare them to state-of-the-art conventional solutions.
Efficient Neuromorphic Signal Processing with Loihi 2
The biologically inspired spiking neurons used in neuromorphic computing are nonlinear filters with dynamic state variables -- very different from the stateless neuron models used in deep learning.
e-TLD: Event-based Framework for Dynamic Object Tracking
On the other hand, when the object re-enters the field-of-view of the camera, a data-driven, global sliding window detector locates the object for subsequent tracking.
Online Few-shot Gesture Learning on a Neuromorphic Processor
We present the Surrogate-gradient Online Error-triggered Learning (SOEL) system for online few-shot learning on neuromorphic processors.
Neuromorphic Nearest-Neighbor Search Using Intel's Pohoiki Springs
Neuromorphic computing applies insights from neuroscience to uncover innovations in computing technology.
A low-power end-to-end hybrid neuromorphic framework for surveillance applications
With the success of deep learning, object recognition systems that can be deployed for real-world applications are becoming commonplace.
On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor
Recent work suggests that synaptic plasticity dynamics in biological models of neurons and neuromorphic hardware are compatible with gradient-based learning (Neftci et al., 2019).
EBBIOT: A Low-complexity Tracking Algorithm for Surveillance in IoVT Using Stationary Neuromorphic Vision Sensors
In this paper, we present EBBIOT-a novel paradigm for object tracking using stationary neuromorphic vision sensors in low-power sensor nodes for the Internet of Video Things (IoVT).
PCA-RECT: An Energy-efficient Object Detection Approach for Event Cameras
We present the first purely event-based, energy-efficient approach for object detection and categorization using an event camera.
Event-based Vision: A Survey
Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur.
SLAYER: Spike Layer Error Reassignment in Time
Configuring deep Spiking Neural Networks (SNNs) is an exciting research avenue for low power spike event based computation.
DART: Distribution Aware Retinal Transform for Event-based Cameras
We introduce a generic visual descriptor, termed as distribution aware retinal transform (DART), that encodes the structural context using log-polar grids for event cameras.
Spiking Optical Flow for Event-based Sensors Using IBM's TrueNorth Neurosynaptic System
This paper describes a fully spike-based neural network for optical flow estimation from Dynamic Vision Sensor data.
Bioinspired Visual Motion Estimation
Visual motion estimation is a computationally intensive, but important task for sighted animals.
Fast Neuromimetic Object Recognition using FPGA Outperforms GPU Implementations
Recognition of objects in still images has traditionally been regarded as a difficult computational problem.
HFirst: A Temporal Approach to Object Recognition
The asynchronous nature of these systems frees computation and communication from the rigid predetermined timing enforced by system clocks in conventional systems.
