35,160 research outputs found
An Event-Driven Multi-Kernel Convolution Processor Module for Event-Driven Vision Sensors
Event-Driven vision sensing is a new way of sensing
visual reality in a frame-free manner. This is, the vision sensor
(camera) is not capturing a sequence of still frames, as in conventional
video and computer vision systems. In Event-Driven sensors
each pixel autonomously and asynchronously decides when to
send its address out. This way, the sensor output is a continuous
stream of address events representing reality dynamically continuously
and without constraining to frames. In this paper we present
an Event-Driven Convolution Module for computing 2D convolutions
on such event streams. The Convolution Module has been
designed to assemble many of them for building modular and hierarchical
Convolutional Neural Networks for robust shape and
pose invariant object recognition. The Convolution Module has
multi-kernel capability. This is, it will select the convolution kernel
depending on the origin of the event. A proof-of-concept test prototype
has been fabricated in a 0.35 m CMOS process and extensive
experimental results are provided. The Convolution Processor has
also been combined with an Event-Driven Dynamic Vision Sensor
(DVS) for high-speed recognition examples. The chip can discriminate
propellers rotating at 2 k revolutions per second, detect symbols
on a 52 card deck when browsing all cards in 410 ms, or detect
and follow the center of a phosphor oscilloscope trace rotating at
5 KHz.Unión Europea 216777 (NABAB)Ministerio de Ciencia e Innovación TEC2009-10639-C04-0
GeNN: a code generation framework for accelerated brain simulations
Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ.
GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials,
Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/
Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection
In this paper, we introduce and evaluate PROPEDEUTICA, a novel methodology
and framework for efficient and effective real-time malware detection,
leveraging the best of conventional machine learning (ML) and deep learning
(DL) algorithms. In PROPEDEUTICA, all software processes in the system start
execution subjected to a conventional ML detector for fast classification. If a
piece of software receives a borderline classification, it is subjected to
further analysis via more performance expensive and more accurate DL methods,
via our newly proposed DL algorithm DEEPMALWARE. Further, we introduce delays
to the execution of software subjected to deep learning analysis as a way to
"buy time" for DL analysis and to rate-limit the impact of possible malware in
the system. We evaluated PROPEDEUTICA with a set of 9,115 malware samples and
877 commonly used benign software samples from various categories for the
Windows OS. Our results show that the false positive rate for conventional ML
methods can reach 20%, and for modern DL methods it is usually below 6%.
However, the classification time for DL can be 100X longer than conventional ML
methods. PROPEDEUTICA improved the detection F1-score from 77.54% (conventional
ML method) to 90.25%, and reduced the detection time by 54.86%. Further, the
percentage of software subjected to DL analysis was approximately 40% on
average. Further, the application of delays in software subjected to ML reduced
the detection time by approximately 10%. Finally, we found and discussed a
discrepancy between the detection accuracy offline (analysis after all traces
are collected) and on-the-fly (analysis in tandem with trace collection). Our
insights show that conventional ML and modern DL-based malware detectors in
isolation cannot meet the needs of efficient and effective malware detection:
high accuracy, low false positive rate, and short classification time.Comment: 17 pages, 7 figure
Simulation of networks of spiking neurons: A review of tools and strategies
We review different aspects of the simulation of spiking neural networks. We
start by reviewing the different types of simulation strategies and algorithms
that are currently implemented. We next review the precision of those
simulation strategies, in particular in cases where plasticity depends on the
exact timing of the spikes. We overview different simulators and simulation
environments presently available (restricted to those freely available, open
source and documented). For each simulation tool, its advantages and pitfalls
are reviewed, with an aim to allow the reader to identify which simulator is
appropriate for a given task. Finally, we provide a series of benchmark
simulations of different types of networks of spiking neurons, including
Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based
or conductance-based synapses, using clock-driven or event-driven integration
strategies. The same set of models are implemented on the different simulators,
and the codes are made available. The ultimate goal of this review is to
provide a resource to facilitate identifying the appropriate integration
strategy and simulation tool to use for a given modeling problem related to
spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of
Computational Neuroscience, in press (2007
- …