19 research outputs found
ADaPTION: Toolbox and Benchmark for Training Convolutional Neural Networks with Reduced Numerical Precision Weights and Activation
Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) are
useful for many practical tasks in machine learning. Synaptic weights, as well
as neuron activation functions within the deep network are typically stored
with high-precision formats, e.g. 32 bit floating point. However, since storage
capacity is limited and each memory access consumes power, both storage
capacity and memory access are two crucial factors in these networks. Here we
present a method and present the ADaPTION toolbox to extend the popular deep
learning library Caffe to support training of deep CNNs with reduced numerical
precision of weights and activations using fixed point notation. ADaPTION
includes tools to measure the dynamic range of weights and activations. 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. The
quantization, especially of the activations, leads to increase of up to 50% of
sparsity especially in early and intermediate layers, which we exploit to skip
multiplications with zero, thus performing faster and computationally cheaper
inference.Comment: 10 pages, 5 figure
Finding the Gap:Neuromorphic Motion Vision in Cluttered Environments
Many animals meander in environments and avoid collisions. How the underlying
neuronal machinery can yield robust behaviour in a variety of environments
remains unclear. In the fly brain, motion-sensitive neurons indicate the
presence of nearby objects and directional cues are integrated within an area
known as the central complex. Such neuronal machinery, in contrast with the
traditional stream-based approach to signal processing, uses an event-based
approach, with events occurring when changes are sensed by the animal. Contrary
to von Neumann computing architectures, event-based neuromorphic hardware is
designed to process information in an asynchronous and distributed manner.
Inspired by the fly brain, we model, for the first time, a neuromorphic
closed-loop system mimicking essential behaviours observed in flying insects,
such as meandering in clutter and gap crossing, which are highly relevant for
autonomous vehicles. We implemented our system both in software and on
neuromorphic hardware. While moving through an environment, our agent perceives
changes in its surroundings and uses this information for collision avoidance.
The agent's manoeuvres result from a closed action-perception loop implementing
probabilistic decision-making processes. This loop-closure is thought to have
driven the development of neural circuitry in biological agents since the
Cambrian explosion. In the fundamental quest to understand neural computation
in artificial agents, we come closer to understanding and modelling biological
intelligence by closing the loop also in neuromorphic systems. As a closed-loop
system, our system deepens our understanding of processing in neural networks
and computations in biological and artificial systems. With these
investigations, we aim to set the foundations for neuromorphic intelligence in
the future, moving towards leveraging the full potential of neuromorphic
systems.Comment: 7 main pages with two figures, including appendix 26 pages with 14
figure
NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps
Convolutional neural networks (CNNs) have become the dominant neural network
architecture for solving many state-of-the-art (SOA) visual processing tasks.
Even though Graphical Processing Units (GPUs) are most often used in training
and deploying CNNs, their power efficiency is less than 10 GOp/s/W for
single-frame runtime inference. We propose a flexible and efficient CNN
accelerator architecture called NullHop that implements SOA CNNs useful for
low-power and low-latency application scenarios. NullHop exploits the sparsity
of neuron activations in CNNs to accelerate the computation and reduce memory
requirements. The flexible architecture allows high utilization of available
computing resources across kernel sizes ranging from 1x1 to 7x7. NullHop can
process up to 128 input and 128 output feature maps per layer in a single pass.
We implemented the proposed architecture on a Xilinx Zynq FPGA platform and
present results showing how our implementation reduces external memory
transfers and compute time in five different CNNs ranging from small ones up to
the widely known large VGG16 and VGG19 CNNs. Post-synthesis simulations using
Mentor Modelsim in a 28nm process with a clock frequency of 500 MHz show that
the VGG19 network achieves over 450 GOp/s. 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. As further proof of NullHop's usability, we interfaced its FPGA
implementation with a neuromorphic event camera for real time interactive
demonstrations
Bioinspired event-driven collision avoidance algorithm based on optic flow
Milde MB, Bertrand O, Benosman R, Egelhaaf M, Chicca E. Bioinspired event-driven collision avoidance algorithm based on optic flow. In: 2015 International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP). IEEE; 2015.Any mobile agent, whether biological or robotic, needs to avoid collisions with obstacles. Insects, such as bees and flies, use optic flow to estimate the relative nearness to obstacles. Optic flow induced by ego-motion is composed of a translational and a rotational component. The segregation of both components is computationally and thus energetically expensive. Flies and bees actively separate the rotational and translational optic flow components via behaviour, i.e. by employing a saccadic strategy of flight and gaze control. Although robotic systems are able to mimic this gaze-strategy, the calculation of optic-flow fields from standard camera images remains time and energy consuming. To overcome this problem, we use a dynamic vision sensor (DVS), which provides event-based information about changes in contrast over time at each pixel location. To extract optic flow from this information, a plane-fitting algorithm estimating the relative velocity in a small spatio-temporal cuboid is used. The depthstructure is derived from the translational optic flow by using local properties of the retina. A collision avoidance direction is then computed from the event-based depth-structure of the environment. The system has successfully been tested on a robotic platform in open-loop
Live Demo: E2P–Events to Polarization Reconstruction from PDAVIS Events
This demonstration shows live operation of of PDAVIS polarization event camera reconstruction by the E2P DNN reported in the main CVPR conference paper Deep Polarization Reconstruction with PDAVIS Events (paper 9149 [7]). Demo code: github.com/SensorsINI/e2
Neuromorphic engineering needs closed-loop benchmarks
Neuromorphic engineering aims to build (autonomous) systems by mimicking biological systems. It is motivated by the observation that biological organisms—from algae to primates—excel in sensing their environment, reacting promptly to their perils and opportunities. Furthermore, they do so more resiliently than our most advanced machines, at a fraction of the power consumption. It follows that the performance of neuromorphic systems should be evaluated in terms of real-time operation, power consumption, and resiliency to real-world perturbations and noise using task-relevant evaluation metrics. Yet, following in the footsteps of conventional machine learning, most neuromorphic benchmarks rely on recorded datasets that foster sensing accuracy as the primary measure for performance. Sensing accuracy is but an arbitrary proxy for the actual system's goal—taking a good decision in a timely manner. Moreover, static datasets hinder our ability to study and compare closed-loop sensing and control strategies that are central to survival for biological organisms. This article makes the case for a renewed focus on closed-loop benchmarks involving real-world tasks. Such benchmarks will be crucial in developing and progressing neuromorphic Intelligence. The shift towards dynamic real-world benchmarking tasks should usher in richer, more resilient, and robust artificially intelligent systems in the future
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Antiskyrmions and their electrical footprint in crystalline mesoscale structures of Mn1.4PtSn
Skyrmionic materials hold the potential for future information technologies, such as racetrack memories. Key to that advancement are systems that exhibit high tunability and scalability, with stored information being easy to read and write by means of all-electrical techniques. Topological magnetic excitations such as skyrmions and antiskyrmions, give rise to a characteristic topological Hall effect. However, the electrical detection of antiskyrmions, in both thin films and bulk samples has been challenging to date. Here, we apply magneto-optical microscopy combined with electrical transport to explore the antiskyrmion phase as it emerges in crystalline mesoscale structures of the Heusler magnet Mn1.4PtSn. We reveal the Hall signature of antiskyrmions in line with our theoretical model, comprising anomalous and topological components. We examine its dependence on the vertical device thickness, field orientation, and temperature. Our atomistic simulations and experimental anisotropy studies demonstrate the link between antiskyrmions and a complex magnetism that consists of competing ferromagnetic, antiferromagnetic, and chiral exchange interactions, not captured by micromagnetic simulations
Obstacle Avoidance and Target Acquisition for Robot Navigation Using a Mixed Signal Analog/Digital Neuromorphic Processing System
Neuromorphic hardware emulates dynamics of biological neural networks in electronic circuits offering an alternative to the von Neumann computing architecture that is low-power, inherently parallel, and event-driven. This hardware allows to implement neural-network based robotic controllers in an energy-efficient way with low latency, but requires solving the problem of device variability, characteristic for analog electronic circuits. In this work, we interfaced a mixed-signal analog-digital neuromorphic processor ROLLS to a neuromorphic dynamic vision sensor (DVS) mounted on a robotic vehicle and developed an autonomous neuromorphic agent that is able to perform neurally inspired obstacle-avoidance and target acquisition. We developed a neural network architecture that can cope with device variability and verified its robustness in different environmental situations, e.g., moving obstacles, moving target, clutter, and poor light conditions. We demonstrate how this network, combined with the properties of the DVS, allows the robot to avoid obstacles using a simple biologically-inspired dynamics. We also show how a Dynamic Neural Field for target acquisition can be implemented in spiking neuromorphic hardware. This work demonstrates an implementation of working obstacle avoidance and target acquisition using mixed signal analog/digital neuromorphic hardware
Spiking Elementary Motion Detector in Neuromorphic Systems
Milde MB, Bertrand O, Ramachandran H, Egelhaaf M, Chicca E. Spiking Elementary Motion Detector in Neuromorphic Systems. Neural Computation. 2018;30(9):2384-2417