Publicaciones del IMSE

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Autor: Miguel Soto Rodríguez
Año: Desde 2002

Artículos de revistas


On practical issues for stochastic STDP hardware with 1-bit synaptic weights
A. Yousefzadeh, E. Stromatias, M. Soto, T. Serrano-Gotarredona and B. Linares-Barranco
Journal Paper · Frontiers in Neuroscience, vol. 12, article 665, 2018
resumen      doi      pdf

In computational neuroscience, synaptic plasticity learning rules are typically studied using the full 64-bit floating point precision computers provide. However, for dedicated hardware implementations, the precision used not only penalizes directly the required memory resources, but also the computing, communication, and energy resources. When it comes to hardware engineering, a key question is always to find the minimum number of necessary bits to keep the neurocomputational system working satisfactorily. Here we present some techniques and results obtained when limiting synaptic weights to 1-bit precision, applied to a Spike-Timing-Dependent-Plasticity (STDP) learning rule in Spiking Neural Networks (SNN). We first illustrate the 1-bit synapses STDP operation by replicating a classical biological experiment on visual orientation tuning, using a simple four neuron setup. After this, we apply 1-bit STDP learning to the hidden feature extraction layer of a 2-layer system, where for the second (and output) layer we use already reported SNN classifiers. The systems are tested on two spiking datasets: a Dynamic Vision Sensor (DVS) recorded poker card symbols dataset and a Poisson-distributed spike representation MNIST dataset version. Tests are performed using the in-house MegaSim event-driven behavioral simulator and by implementing the systems on FPGA (Field Programmable Gate Array) hardware.

An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data
E. Stromatias, M. Soto, T. Serrano-Gotarredona and B. Linares-Barranco
Journal Paper · Frontiers in Neuroscience, vol. 11, article 350, 2017
resumen      doi      pdf

This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN) System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS) chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77%) and Poker-DVS (100%) real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.

Congresos


Scene Context Classification with Event-Driven Spiking Deep Neural Networks
P. Negri, M. Soto, B. Linares-Barranco and T. Serrano-Gotarredona
Conference · IEEE International Conference on Electronics Circuits and Systems ICECS 2018
resumen     

Event-Driven computation is attracting growing attention among researchers for several reasons. On one hand, the availability of new bio-inspired retina-like vision sensors that provide spiking outputs, like the Dynamic Vision Sensor (DVS) makes it possible to demonstrate energy efficient and high-speed complex vision tasks. On the other hand, the emergence of abundant new nanoscale devices that operate as tunable two-terminal resistive elements, which when operated through dynamic pulsing techniques emulate learning and processing in the brain, promise an explosion of highly compact energy efficient neuromorphic event-driven applications. In this paper, we focus for the first time on a high-level cognitive task, namely scene context classification, performed by event-driven computations and using real sensory data from a DVS camera.

Performance Comparison of Time-Step-Driven Versus Event-Driven Neural State Update Approaches in Spinnaker
M. Soto, A. Yousefzadeh, T. Serrano-Gotarredona, F. Galluppi, L. Plana, S. Furber and B. Linares-Barranco
Conference · IEEE International Symposium on Circuits and Systems ISCAS 2018
resumen     

The SpiNNaker chip is a multi-core processor optimized for neuromorphic applications. Many SpiNNaker chips are assembled to make a highly parallel million core platform. This system can be used for simulation of a large number of neurons in real-time. SpiNNaker is using a general purpose ARM processor that gives a high amount of flexibility to implement different methods for processing spikes. Various libraries and packages are provided to translate a high-level description of Spiking Neural Networks (SNN) to low-level machine language that can be used in the ARM processors. In this paper, we introduce and compare three different methods to implement this intermediate layer of abstraction. We have examined the advantages of each method by various criteria, which can be useful for professional users to choose between them. All the codes that are used in this paper are available for academic propose.

An Intrinsic Method for Fast Parameter Update on the Spinnaker Platform
M. Soto, T. Serrano-Gotarredona and B. Linares-Barranco
Conference · IEEE International Symposium on Circuits and Systems ISCAS 2018
resumen     

Neuromorphic Computing or Spiking (also called Event-Driven) Neural Systems are becoming of high interest as they potentially allow for lower power hardware computing platforms, where power consumption is data driven. Traditional approaches (both in software and in hardware), which are not data driven, rely on generic system state updates, consuming a fixed amount of computing resources at each step, independent on the data itself. In neuromorphic spiking or (event-driven) computing systems power is consumed (in principle) if new data is transferred, either at the system input, system output, or internally between computing nodes. One such neuromorphic event-driven computing platform is the scalable SpiNNaker system, which is aimed for a million ARM core platform, capable of emulating in the order of a billion neurons in real time. An important practical drawback of the platform is the long time it takes to download to the hardware a given computational architecture. This step has to be repeated even if one wants to update a set of parameters. Here we present a method for updating internal parameters without downloading again the full architecture, by adding special neurons into the computing architecture which when they spike change given parameters. This allows to download the computing architecture only once to the SpiNNaker platform, and then take advantage of its highly efficient communication network to command specific parameter changes. This allows for intensive parameter searches in a more efficient manner.

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