355 research outputs found
HyNNA: Improved Performance for Neuromorphic Vision Sensor based Surveillance using Hybrid Neural Network Architecture
Applications in the Internet of Video Things (IoVT) domain have very tight
constraints with respect to power and area. While neuromorphic vision sensors
(NVS) may offer advantages over traditional imagers in this domain, the
existing NVS systems either do not meet the power constraints or have not
demonstrated end-to-end system performance. To address this, we improve on a
recently proposed hybrid event-frame approach by using morphological image
processing algorithms for region proposal and address the low-power requirement
for object detection and classification by exploring various convolutional
neural network (CNN) architectures. Specifically, we compare the results
obtained from our object detection framework against the state-of-the-art
low-power NVS surveillance system and show an improved accuracy of 82.16% from
63.1%. Moreover, we show that using multiple bits does not improve accuracy,
and thus, system designers can save power and area by using only single bit
event polarity information. In addition, we explore the CNN architecture space
for object classification and show useful insights to trade-off accuracy for
lower power using lesser memory and arithmetic operations.Comment: 4 pages, 2 figure
Event-based Vision: A Survey
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 changes, and output a stream of events that encode
the time, location and sign of the brightness changes. 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. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
Neuromorphic-P2M: Processing-in-Pixel-in-Memory Paradigm for Neuromorphic Image Sensors
Edge devices equipped with computer vision must deal with vast amounts of
sensory data with limited computing resources. Hence, researchers have been
exploring different energy-efficient solutions such as near-sensor processing,
in-sensor processing, and in-pixel processing, bringing the computation closer
to the sensor. In particular, in-pixel processing embeds the computation
capabilities inside the pixel array and achieves high energy efficiency by
generating low-level features instead of the raw data stream from CMOS image
sensors. Many different in-pixel processing techniques and approaches have been
demonstrated on conventional frame-based CMOS imagers, however, the
processing-in-pixel approach for neuromorphic vision sensors has not been
explored so far. In this work, we for the first time, propose an asynchronous
non-von-Neumann analog processing-in-pixel paradigm to perform convolution
operations by integrating in-situ multi-bit multi-channel convolution inside
the pixel array performing analog multiply and accumulate (MAC) operations that
consume significantly less energy than their digital MAC alternative. To make
this approach viable, we incorporate the circuit's non-ideality, leakage, and
process variations into a novel hardware-algorithm co-design framework that
leverages extensive HSpice simulations of our proposed circuit using the GF22nm
FD-SOI technology node. We verified our framework on state-of-the-art
neuromorphic vision sensor datasets and show that our solution consumes ~2x
lower backend-processor energy while maintaining almost similar front-end
(sensor) energy on the IBM DVS128-Gesture dataset than the state-of-the-art
while maintaining a high test accuracy of 88.36%.Comment: 17 pages, 11 figures, 2 table
NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking
The field of neuromorphic computing holds great promise in terms of advancing
computing efficiency and capabilities by following brain-inspired principles.
However, the rich diversity of techniques employed in neuromorphic research has
resulted in a lack of clear standards for benchmarking, hindering effective
evaluation of the advantages and strengths of neuromorphic methods compared to
traditional deep-learning-based methods. This paper presents a collaborative
effort, bringing together members from academia and the industry, to define
benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are
to be a collaborative, fair, and representative benchmark suite developed by
the community, for the community. In this paper, we discuss the challenges
associated with benchmarking neuromorphic solutions, and outline the key
features of NeuroBench. We believe that NeuroBench will be a significant step
towards defining standards that can unify the goals of neuromorphic computing
and drive its technological progress. Please visit neurobench.ai for the latest
updates on the benchmark tasks and metrics
NeuroBench:Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking
The field of neuromorphic computing holds great promise in terms of advancing computing efficiency and capabilities by following brain-inspired principles. However, the rich diversity of techniques employed in neuromorphic research has resulted in a lack of clear standards for benchmarking, hindering effective evaluation of the advantages and strengths of neuromorphic methods compared to traditional deep-learning-based methods. This paper presents a collaborative effort, bringing together members from academia and the industry, to define benchmarks for neuromorphic computing: NeuroBench. The goals of NeuroBench are to be a collaborative, fair, and representative benchmark suite developed by the community, for the community. In this paper, we discuss the challenges associated with benchmarking neuromorphic solutions, and outline the key features of NeuroBench. We believe that NeuroBench will be a significant step towards defining standards that can unify the goals of neuromorphic computing and drive its technological progress. Please visit neurobench.ai for the latest updates on the benchmark tasks and metrics
Human activity recognition: suitability of a neuromorphic approach for on-edge AIoT applications
Human activity recognition (HAR) is a classification problem involving time-dependent signals produced by body monitoring, and its application domain covers all the aspects of human life, from healthcare to sport, from safety to smart environments. As such, it is naturally well suited for on-edge deployment of personalized point-of-care (POC) analyses or other tailored services for the user. However, typical smart and wearable devices suffer from relevant limitations regarding energy consumption, and this significantly hinders the possibility for successful employment of edge computing for tasks like HAR. In this paper, we investigate how this problem can be mitigated by adopting a neuromorphic approach. By comparing optimized classifiers based on traditional deep neural network (DNN) architectures as well as on recent alternatives like the Legendre Memory Unit (LMU), we show how spiking neural networks (SNNs) can effectively deal with the temporal signals typical of HAR providing high performances at a low energy cost. By carrying out an application-oriented hyperparameter optimization, we also propose a methodology flexible to be extended to different domains, to enlarge the field of neuro-inspired classifier suitable for on-edge artificial intelligence of things (AIoT) applications
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. The DART descriptor is applied to four different
problems, namely object classification, tracking, detection and feature
matching: (1) The DART features are directly employed as local descriptors in a
bag-of-features classification framework and testing is carried out on four
standard event-based object datasets (N-MNIST, MNIST-DVS, CIFAR10-DVS,
NCaltech-101). (2) Extending the classification system, tracking is
demonstrated using two key novelties: (i) For overcoming the low-sample problem
for the one-shot learning of a binary classifier, statistical bootstrapping is
leveraged with online learning; (ii) To achieve tracker robustness, the scale
and rotation equivariance property of the DART descriptors is exploited for the
one-shot learning. (3) To solve the long-term object tracking problem, an
object detector is designed using the principle of cluster majority voting. The
detection scheme is then combined with the tracker to result in a high
intersection-over-union score with augmented ground truth annotations on the
publicly available event camera dataset. (4) Finally, the event context encoded
by DART greatly simplifies the feature correspondence problem, especially for
spatio-temporal slices far apart in time, which has not been explicitly tackled
in the event-based vision domain.Comment: 12 pages, revision submitted to TPAMI in Nov 201
EBBINNOT: A Hardware Efficient Hybrid Event-Frame Tracker for Stationary Dynamic Vision Sensors
As an alternative sensing paradigm, dynamic vision sensors (DVS) have been
recently explored to tackle scenarios where conventional sensors result in high
data rate and processing time. This paper presents a hybrid event-frame
approach for detecting and tracking objects recorded by a stationary
neuromorphic sensor, thereby exploiting the sparse DVS output in a low-power
setting for traffic monitoring. Specifically, we propose a hardware efficient
processing pipeline that optimizes memory and computational needs that enable
long-term battery powered usage for IoT applications. To exploit the background
removal property of a static DVS, we propose an event-based binary image
creation that signals presence or absence of events in a frame duration. This
reduces memory requirement and enables usage of simple algorithms like median
filtering and connected component labeling for denoise and region proposal
respectively. To overcome the fragmentation issue, a YOLO inspired neural
network based detector and classifier to merge fragmented region proposals has
been proposed. Finally, a new overlap based tracker was implemented, exploiting
overlap between detections and tracks is proposed with heuristics to overcome
occlusion. The proposed pipeline is evaluated with more than 5 hours of traffic
recording spanning three different locations on two different neuromorphic
sensors (DVS and CeleX) and demonstrate similar performance. Compared to
existing event-based feature trackers, our method provides similar accuracy
while needing approx 6 times less computes. To the best of our knowledge, this
is the first time a stationary DVS based traffic monitoring solution is
extensively compared to simultaneously recorded RGB frame-based methods while
showing tremendous promise by outperforming state-of-the-art deep learning
solutions.Comment: 16 pages, 13 figure
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