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Found 32 papers, 7 papers with code
Exploiting Symmetric Temporally Sparse BPTT for Efficient RNN Training
Implementing online training of RNNs on the edge calls for optimized algorithms for an efficient deployment on hardware.
Shining light on the DVS pixel: A tutorial and discussion about biasing and optimization
The operation of the DVS event camera is controlled by the user through adjusting different bias parameters.
Optimal biasing and physical limits of DVS event noise
Under dim lighting conditions, the output of Dynamic Vision Sensor (DVS) event cameras is strongly affected by noise.
Exploiting Alternating DVS Shot Noise Event Pair Statistics to Reduce Background Activity
Dynamic Vision Sensors (DVS) record "events" corresponding to pixel-level brightness changes, resulting in data-efficient representation of a dynamic visual scene.
Deep Polarization Reconstruction With PDAVIS Events
The polarization event camera PDAVIS is a novel bio-inspired neuromorphic vision sensor that reports both conventional polarization frames and asynchronous, continuously per-pixel polarization brightness changes (polarization events) with fast temporal resolution and large dynamic range.
Utility and Feasibility of a Center Surround Event Camera
Standard dynamic vision sensor (DVS) event cameras output a stream of spatially-independent log-intensity brightness change events so they cannot suppress spatial redundancy.
Bio-inspired Polarization Event Camera
The stomatopod (mantis shrimp) visual system has recently provided a blueprint for the design of paradigm-shifting polarization and multispectral imaging sensors, enabling solutions to challenging medical and remote sensing problems.
Unraveling the paradox of intensity-dependent DVS pixel noise
While measurements of the logarithmic photoreceptor predict that the photoreceptor is approximately a first-order system with RMS noise voltage independent of the photocurrent, DVS output shows higher noise event rates at low light intensity.
Spartus: A 9.4 TOp/s FPGA-based LSTM Accelerator Exploiting Spatio-Temporal Sparsity
The pruned networks running on Spartus hardware achieve weight sparsity levels of up to 96% and 94% with negligible accuracy loss on the TIMIT and the Librispeech datasets.
Feedback control of event cameras
Dynamic vision sensor event cameras produce a variable data rate stream of brightness change events.
v2e: From Video Frames to Realistic DVS Events
The first experiment is object recognition with N-Caltech 101 dataset.
DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction
The dataset was captured with a DAVIS camera that concurrently streams both dynamic vision sensor (DVS) brightness change events and active pixel sensor (APS) intensity frames.
Data-Driven Neuromorphic DRAM-based CNN and RNN Accelerators
The energy consumed by running large deep neural networks (DNNs) on hardware accelerators is dominated by the need for lots of fast memory to store both states and weights.
Learning to Exploit Multiple Vision Modalities by Using Grafted Networks
This paper proposes a Network Grafting Algorithm (NGA), where a new front end network driven by unconventional visual inputs replaces the front end network of a pretrained deep network that processes intensity frames.
Ranked #4 on
Event-based Object Segmentation
on RGBE-SEG
Recurrent Neural Network Control of a Hybrid Dynamic Transfemoral Prosthesis with EdgeDRNN Accelerator
Lower leg prostheses could improve the life quality of amputees by increasing comfort and reducing energy to locomote, but currently control methods are limited in modulating behaviors based upon the human's experience.
EdgeDRNN: Enabling Low-latency Recurrent Neural Network Edge Inference
This paper presents a Gated Recurrent Unit (GRU) based recurrent neural network (RNN) accelerator called EdgeDRNN designed for portable edge computing.
Dynamic Vision Sensor integration on FPGA-based CNN accelerators for high-speed visual classification
The use of dynamic vision sensors (DVS) that emulate the behavior of a biological retina is taking an incremental importance to improve this applications due to its nature, where the information is represented by a continuous stream of spikes and the frames to be processed by the CNN are constructed collecting a fixed number of these spikes (called events).
Closing the Accuracy Gap in an Event-Based Visual Recognition Task
Mobile and embedded applications require neural networks-based pattern recognition systems to perform well under a tight computational budget.
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.
EV-IMO: Motion Segmentation Dataset and Learning Pipeline for Event Cameras
In addition to camera egomotion and a dense depth map, the network estimates pixel-wise independently moving object segmentation and computes per-object 3D translational velocities for moving objects.
PRED18: Dataset and Further Experiments with DAVIS Event Camera in Predator-Prey Robot Chasing
Conventional vision CNNs are driven by camera frames at constant sample rate, thus achieving a fixed latency and power consumption tradeoff.
ABMOF: A Novel Optical Flow Algorithm for Dynamic Vision Sensors
The precise event timing, sparse output, and wide dynamic range of the events are well suited for optical flow, but conventional optical flow (OF) algorithms are not well matched to the event stream data.
ADaPTION: Toolbox and Benchmark for Training Convolutional Neural Networks with Reduced Numerical Precision Weights and Activation
Using the ADaPTION tools, we quantized several CNNs including VGG16 down to 16-bit weights and activations with only 0. 8% drop in Top-1 accuracy.
DDD17: End-To-End DAVIS Driving Dataset
Event cameras, such as dynamic vision sensors (DVS), and dynamic and active-pixel vision sensors (DAVIS) can supplement other autonomous driving sensors by providing a concurrent stream of standard active pixel sensor (APS) images and DVS temporal contrast events.
A Low Power, Fully Event-Based Gesture Recognition System
We present the first gesture recognition system implemented end-to-end on event-based hardware, using a TrueNorth neurosynaptic processor to recognize hand gestures in real-time at low power from events streamed live by a Dynamic Vision Sensor (DVS).
Block-Matching Optical Flow for Dynamic Vision Sensor- Algorithm and FPGA Implementation
Rapid and low power computation of optical flow (OF) is potentially useful in robotics.
NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps
By exploiting sparsity, NullHop achieves an efficiency of 368%, maintains over 98% utilization of the MAC units, and achieves a power efficiency of over 3TOp/s/W in a core area of 6. 3mm$^2$.
Delta Networks for Optimized Recurrent Network Computation
Similarly, on the large Wall Street Journal speech recognition benchmark even existing networks can be greatly accelerated as delta networks, and a 5. 7x improvement with negligible loss of accuracy can be obtained through training.
The Event-Camera Dataset and Simulator: Event-based Data for Pose Estimation, Visual Odometry, and SLAM
New vision sensors, such as the Dynamic and Active-pixel Vision sensor (DAVIS), incorporate a conventional global-shutter camera and an event-based sensor in the same pixel array.
Training Deep Spiking Neural Networks using Backpropagation
Deep spiking neural networks (SNNs) hold great potential for improving the latency and energy efficiency of deep neural networks through event-based computation.
Event-based, 6-DOF Camera Tracking from Photometric Depth Maps
Event cameras are bio-inspired vision sensors that output pixel-level brightness changes instead of standard intensity frames.
Steering a Predator Robot using a Mixed Frame/Event-Driven Convolutional Neural Network
The CNN is trained and run on data from a Dynamic and Active Pixel Sensor (DAVIS) mounted on a Summit XL robot (the predator), which follows another one (the prey).
