134 research outputs found
Event Probability Mask (EPM) and Event Denoising Convolutional Neural Network (EDnCNN) for Neuromorphic Cameras
This paper presents a novel method for labeling real-world neuromorphic
camera sensor data by calculating the likelihood of generating an event at each
pixel within a short time window, which we refer to as "event probability mask"
or EPM. Its applications include (i) objective benchmarking of event denoising
performance, (ii) training convolutional neural networks for noise removal
called "event denoising convolutional neural network" (EDnCNN), and (iii)
estimating internal neuromorphic camera parameters. We provide the first
dataset (DVSNOISE20) of real-world labeled neuromorphic camera events for noise
removal.Comment: submitted to CVPR 202
Algorithm Hardware Codesign for High Performance Neuromorphic Computing
Driven by the massive application of Internet of Things (IoT), embedded system and Cyber Physical System (CPS) etc., there is an increasing demand to apply machine intelligence on these power limited scenarios. Though deep learning has achieved impressive performance on various realistic and practical tasks such as anomaly detection, pattern recognition, machine vision etc., the ever-increasing computational complexity and model size of Deep Neural Networks (DNN) make it challenging to deploy them onto aforementioned scenarios where computation, memory and energy resource are all limited. Early studies show that biological systems\u27 energy efficiency can be orders of magnitude higher than that of digital systems. Hence taking inspiration from biological systems, neuromorphic computing and Spiking Neural Network (SNN) have drawn attention as alternative solutions for energy-efficient machine intelligence.
Though believed promising, neuromorphic computing are hardly used for real world applications. A major problem is that the performance of SNN is limited compared with DNNs due to the lack of efficient training algorithm. In SNN, neuron\u27s output is spike, which is represented by Dirac Delta function mathematically. Becauase of the non-differentiable nature of spike, gradient descent cannot be directly used to train SNN. Hence algorithm level innovation is desirable. Next, as an emerging computing paradigm, hardware and architecture level innovation is also required to support new algorithms and to explore the potential of neuromorphic computing.
In this work, we present a comprehensive algorithm-hardware codesign for neuromorphic computing. On the algorithm side, we address the training difficulty. We first derive a flexible SNN model that retains critical neural dynamics, and then develop algorithm to train SNN to learn temporal patterns. Next, we apply proposed algorithm to multivariate time series classification tasks to demonstrate its advantages. On hardware level, we develop a systematic solution on FPGA that is optimized for proposed SNN model to enable high performance inference. In addition, we also explore emerging devices, a memristor-based neuromorphic design is proposed. We carry out a neuron and synapse circuit which can replicate the important neural dynamics such as filter effect and adaptive threshold
EV-Planner: Energy-Efficient Robot Navigation via Event-Based Physics-Guided Neuromorphic Planner
Vision-based object tracking is an essential precursor to performing
autonomous aerial navigation in order to avoid obstacles. Biologically inspired
neuromorphic event cameras are emerging as a powerful alternative to
frame-based cameras, due to their ability to asynchronously detect varying
intensities (even in poor lighting conditions), high dynamic range, and
robustness to motion blur. Spiking neural networks (SNNs) have gained traction
for processing events asynchronously in an energy-efficient manner. On the
other hand, physics-based artificial intelligence (AI) has gained prominence
recently, as they enable embedding system knowledge via physical modeling
inside traditional analog neural networks (ANNs). In this letter, we present an
event-based physics-guided neuromorphic planner (EV-Planner) to perform
obstacle avoidance using neuromorphic event cameras and physics-based AI. We
consider the task of autonomous drone navigation where the mission is to detect
moving gates and fly through them while avoiding a collision. We use event
cameras to perform object detection using a shallow spiking neural network in
an unsupervised fashion. Utilizing the physical equations of the brushless DC
motors present in the drone rotors, we train a lightweight energy-aware
physics-guided neural network with depth inputs. This predicts the optimal
flight time responsible for generating near-minimum energy paths. We spawn the
drone in the Gazebo simulator and implement a sensor-fused vision-to-planning
neuro-symbolic framework using Robot Operating System (ROS). Simulation results
for safe collision-free flight trajectories are presented with performance
analysis and potential future research direction
Exploring the landscapes of "computing": digital, neuromorphic, unconventional -- and beyond
The acceleration race of digital computing technologies seems to be steering
toward impasses -- technological, economical and environmental -- a condition
that has spurred research efforts in alternative, "neuromorphic" (brain-like)
computing technologies. Furthermore, since decades the idea of exploiting
nonlinear physical phenomena "directly" for non-digital computing has been
explored under names like "unconventional computing", "natural computing",
"physical computing", or "in-materio computing". This has been taking place in
niches which are small compared to other sectors of computer science. In this
paper I stake out the grounds of how a general concept of "computing" can be
developed which comprises digital, neuromorphic, unconventional and possible
future "computing" paradigms. The main contribution of this paper is a
wide-scope survey of existing formal conceptualizations of "computing". The
survey inspects approaches rooted in three different kinds of background
mathematics: discrete-symbolic formalisms, probabilistic modeling, and
dynamical-systems oriented views. It turns out that different choices of
background mathematics lead to decisively different understandings of what
"computing" is. Across all of this diversity, a unifying coordinate system for
theorizing about "computing" can be distilled. Within these coordinates I
locate anchor points for a foundational formal theory of a future
computing-engineering discipline that includes, but will reach beyond, digital
and neuromorphic computing.Comment: An extended and carefully revised version of this manuscript has now
(March 2021) been published as "Toward a generalized theory comprising
digital, neuromorphic, and unconventional computing" in the new open-access
journal Neuromorphic Computing and Engineerin
Can my chip behave like my brain?
Many decades ago, Carver Mead established the foundations of neuromorphic systems. Neuromorphic systems are analog circuits that emulate biology. These circuits utilize subthreshold dynamics of CMOS transistors to mimic the behavior of neurons. The objective is to not only simulate the human brain, but also to build useful applications using these bio-inspired circuits for ultra low power speech processing, image processing, and robotics. This can be achieved using reconfigurable hardware, like field programmable analog arrays (FPAAs), which enable configuring different applications on a cross platform system. As digital systems saturate in terms of power efficiency, this alternate approach has the potential to improve computational efficiency by approximately eight orders of magnitude. These systems, which include analog, digital, and neuromorphic elements combine to result in a very powerful reconfigurable processing machine.Ph.D
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