2.3k citations · 3.2k across the 16 of their papers we have counts for
20 papers
Emulating Brain-like Rapid Learning in Neuromorphic Edge Computing
Kenneth Stewart, Michael Neumeier, Sumit Bam Shrestha +2
Achieving personalized intelligence at the edge with real-time learning capabilities holds enormous promise in enhancing our daily experiences and helping decision making, planning…
Ultra-low-power Image Classification on Neuromorphic Hardware
Gregor Lenz, Garrick Orchard, Sadique Sheik
Spiking neural networks (SNNs) promise ultra-low-power applications by exploiting temporal and spatial sparsity. The number of binary activations, called spikes, is proportional to…
The Intel Neuromorphic DNS Challenge
Jonathan Timcheck, Sumit Bam Shrestha, Daniel Ben Dayan Rubin +5
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…
Efficient Neuromorphic Signal Processing with Loihi 2
Garrick Orchard, E. Paxon Frady, Daniel Ben Dayan Rubin +4
The biologically inspired spiking neurons used in neuromorphic computing are nonlinear filters with dynamic state variables -- very different from the stateless neuron models used…
e-TLD: Event-based Framework for Dynamic Object Tracking
Bharath Ramesh, Shihao Zhang, Hong Yang +4
This paper presents a long-term object tracking framework with a moving event camera under general tracking conditions. A first of its kind for these revolutionary cameras, the tra…
Online Few-shot Gesture Learning on a Neuromorphic Processor
Kenneth Stewart, Garrick Orchard, Sumit Bam Shrestha +1
We present the Surrogate-gradient Online Error-triggered Learning (SOEL) system for online few-shot learning on neuromorphic processors. The SOEL learning system uses a combination…
Neuromorphic Nearest-Neighbor Search Using Intel's Pohoiki Springs
E. Paxon Frady, Garrick Orchard, David Florey +7
Neuromorphic computing applies insights from neuroscience to uncover innovations in computing technology. In the brain, billions of interconnected neurons perform rapid computation…
On-chip Few-shot Learning with Surrogate Gradient Descent on a Neuromorphic Processor
Kenneth Stewart, Garrick Orchard, Sumit Bam Shrestha +1
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
Jyotibdha Acharya, Andres Ussa Caycedo, Vandana Reddy Padala +4
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)…
A low-power end-to-end hybrid neuromorphic framework for surveillance applications
Andres Ussa, Luca Della Vedova, Vandana Reddy Padala +6
With the success of deep learning, object recognition systems that can be deployed for real-world applications are becoming commonplace. However, inference that needs to largely ta…
Analog Weights in ReRAM DNN Accelerators
Jason K. Eshraghian, Sung-Mo Kang, Seungbum Baek +3
Artificial neural networks have become ubiquitous in modern life, which has triggered the emergence of a new class of application specific integrated circuits for their acceleratio…
PCA-RECT: An Energy-efficient Object Detection Approach for Event Cameras
Bharath Ramesh, Andres Ussa, Luca Della Vedova +2
We present the first purely event-based, energy-efficient approach for object detection and categorization using an event camera. Compared to traditional frame-based cameras, choos…
Event-based Vision: A Survey
Guillermo Gallego, Tobi Delbruck, Garrick Orchard +8
Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness ch…
SLAYER: Spike Layer Error Reassignment in Time
Sumit Bam Shrestha, Garrick Orchard
Configuring deep Spiking Neural Networks (SNNs) is an exciting research avenue for low power spike event based computation. However, the spike generation function is non-differenti…
Spiking Optical Flow for Event-based Sensors Using IBM's TrueNorth Neurosynaptic System
Germain Haessig, Andrew Cassidy, Rodrigo Alvarez +2
This paper describes a fully spike-based neural network for optical flow estimation from Dynamic Vision Sensor data. A low power embedded implementation of the method which combine…
DART: Distribution Aware Retinal Transform for Event-based Cameras
Bharath Ramesh, Hong Yang, Garrick Orchard +3
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. The DA…
Fast Neuromimetic Object Recognition using FPGA Outperforms GPU Implementations
Garrick Orchard, Jacob G. Martin, R. Jacob Vogelstein +1
Recognition of objects in still images has traditionally been regarded as a difficult computational problem. Although modern automated methods for visual object recognition have ac…
Bioinspired Visual Motion Estimation
Garrick Orchard, Ralph Etienne-Cummings
Visual motion estimation is a computationally intensive, but important task for sighted animals. Replicating the robustness and efficiency of biological visual motion estimation in…
HFirst: A Temporal Approach to Object Recognition
Garrick Orchard, Cedric Meyer, Ralph Etienne-Cummings +3
This paper introduces a spiking hierarchical model for object recognition which utilizes the precise timing information inherently present in the output of biologically inspired as…
Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades
Garrick Orchard, Ajinkya Jayawant, Gregory Cohen +1
Creating datasets for Neuromorphic Vision is a challenging task. A lack of available recordings from Neuromorphic Vision sensors means that data must typically be recorded specific…