14 research outputs found
To which extend is the "neural code" a metric ?
Here is proposed a review of the different choices to structure spike trains,
using deterministic metrics. Temporal constraints observed in biological or
computational spike trains are first taken into account. The relation with
existing neural codes (rate coding, rank coding, phase coding, ..) is then
discussed. To which extend the "neural code" contained in spike trains is
related to a metric appears to be a key point, a generalization of the
Victor-Purpura metric family being proposed for temporal constrained causal
spike trainsComment: 5 pages 5 figures Proceeding of the conference NeuroComp200
Event-driven implementation of deep spiking convolutional neural networks for supervised classification using the SpiNNaker neuromorphic platform
Neural networks have enabled great advances in recent times due mainly to improved parallel
computing capabilities in accordance to Moore’s Law, which allowed reducing the time needed for the
parameter learning of complex, multi-layered neural architectures. However, with silicon technology
reaching its physical limits, new types of computing paradigms are needed to increase the power
efficiency of learning algorithms, especially for dealing with deep spatio-temporal knowledge on
embedded applications. With the goal of mimicking the brain’s power efficiency, new hardware
architectures such as the SpiNNaker board have been built. Furthermore, recent works have shown that
networks using spiking neurons as learning units can match classical neural networks in supervised
tasks. In this paper, we show that the implementation of state-of-the-art models on both the MNIST
and the event-based NMNIST digit recognition datasets is possible on neuromorphic hardware. We
use two approaches, by directly converting a classical neural network to its spiking version and by
training a spiking network from scratch. For both cases, software simulations and implementations
into a SpiNNaker 103 machine were performed. Numerical results approaching the state of the art
on digit recognition are presented, and a new method to decrease the spike rate needed for the task
is proposed, which allows a significant reduction of the spikes (up to 34 times for a fully connected
architecture) while preserving the accuracy of the system. With this method, we provide new insights
on the capabilities offered by networks of spiking neurons to efficiently encode spatio-temporal
information.Consejo Nacional de Ciencia Y Tecnología (México) FC2016-1961European Union's Horizon 2020 No 824164 HERMESMinisterio de Ciencia, Innovación y Universidades TEC2015-63884-C2-1-
A SpiNNaker Application: Design, Implementation and Validation of SCPGs
In this paper, we present the numerical results of the implementation
of a Spiking Central Pattern Generator (SCPG) on a SpiNNaker
board. The SCPG is a network of current-based leaky integrateand-
fire (LIF) neurons, which generates periodic spike trains that correspond
to different locomotion gaits (i.e. walk, trot, run). To generate
such patterns, the SCPG has been configured with different topologies,
and its parameters have been experimentally estimated. To validate our
designs, we have implemented them on the SpiNNaker board using PyNN
and we have embedded it on a hexapod robot. The system includes a
Dynamic Vision Sensor system able to command a pattern to the robot
depending on the frequency of the events fired. The more activity the
DVS produces, the faster that the pattern that is commanded will be.Ministerio de Economía y Competitividad TEC2016-77785-
Liquid State Machine on SpiNNaker for Spatio-Temporal Classification Tasks
Liquid State Machines (LSMs) are computing reservoirs composed of recurrently connected Spiking Neural Networks which have attracted research interest for their modeling capacity of biological structures and as promising pattern recognition tools suitable for their implementation in neuromorphic processors, benefited from the modest use of computing resources in their training process. However, it has been difficult to optimize LSMs for solving complex tasks such as event-based computer vision and few implementations in large-scale neuromorphic processors have been attempted. In this
work, we show that offline-trained LSMs implemented in the SpiNNaker neuromorphic processor are able to classify visual events, achieving state-of-the-art performance in the event-based N-MNIST dataset. The training of the readout layer is performed using a recent adaptation of back-propagation-through-time (BPTT) for SNNs, while the internal weights of the reservoir are kept static. Results show that mapping our LSM from a Deep Learning framework to SpiNNaker does not affect the performance of the classification task. Additionally, we show that weight quantization, which substantially reduces the memory footprint of the LSM, has a small impact on its performance
Hardware/Software Co-Design of a Circle Detection System Based on Evolutionary Computing
In recent years, the strategy of co-designing Hardware/Software (HW/SW) systems has been widely adopted to exploit the synergy between both approaches thanks to technological advances that have led to more powerful devices providing an increasingly better cost–benefit trade-off. This paper presents an HW/SW system for the detection of multiple circles in digital images based on a genetic algorithm. It is implemented on an Ultra96-v2 development board, which contains a Xilinx Zynq UltraScale+ MPSoC device and supports a Linux operating system that facilitates application development. The design is powered by developing an interactive computing environment by means of the Jupyter Notebook platform, in which different programming languages coexist. The specific advantages of each of these languages have been used to describe the hardware component that accelerates the evolutionary computation for circle detection (VHDL), to execute SW-HW interaction functions, as well as the pre- and post-processing of the images (ANSI-C) and to code, evaluate, and document the system execution process (Python). As a result, a computationally efficient application was obtained, with high accuracy in the detection of circles in synthetic and real images, and with a high degree of reconfigurability that provides the user with the necessary tools to incorporate it in a specific area of interest
Hardware/Software Co-Design of a Circle Detection System Based on Evolutionary Computing
In recent years, the strategy of co-designing Hardware/Software (HW/SW) systems has been widely adopted to exploit the synergy between both approaches thanks to technological advances that have led to more powerful devices providing an increasingly better cost–benefit trade-off. This paper presents an HW/SW system for the detection of multiple circles in digital images based on a genetic algorithm. It is implemented on an Ultra96-v2 development board, which contains a Xilinx Zynq UltraScale+ MPSoC device and supports a Linux operating system that facilitates application development. The design is powered by developing an interactive computing environment by means of the Jupyter Notebook platform, in which different programming languages coexist. The specific advantages of each of these languages have been used to describe the hardware component that accelerates the evolutionary computation for circle detection (VHDL), to execute SW-HW interaction functions, as well as the pre- and post-processing of the images (ANSI-C) and to code, evaluate, and document the system execution process (Python). As a result, a computationally efficient application was obtained, with high accuracy in the detection of circles in synthetic and real images, and with a high degree of reconfigurability that provides the user with the necessary tools to incorporate it in a specific area of interest