DAMNED: A Distributed and Multithreaded Neural Event-Driven simulation framework

In a Spiking Neural Networks (SNN), spike emissions are sparsely and irregularly distributed both in time and in the network architecture. Since a current feature of SNNs is a low average activity, efficient implementations of SNNs are usually based on an Event-Driven Simulation (EDS). On the other hand, simulations of large scale neural networks can take advantage of distributing the neurons on a set of processors (either workstation cluster or parallel computer). This article presents DAMNED, a large scale SNN simulation framework able to gather the benefits of EDS and parallel computing. Two levels of parallelism are combined: Distributed mapping of the neural topology, at the network level, and local multithreaded allocation of resources for simultaneous processing of events, at the neuron level. Based on the causality of events, a distributed solution is proposed for solving the complex problem of scheduling without synchronization barrier.Comment: 6 page

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems

Parallel Architectures for Planetary Exploration Requirements (PAPER)

The Parallel Architectures for Planetary Exploration Requirements (PAPER) project is essentially research oriented towards technology insertion issues for NASA's unmanned planetary probes. It was initiated to complement and augment the long-term efforts for space exploration with particular reference to NASA/LaRC's (NASA Langley Research Center) research needs for planetary exploration missions of the mid and late 1990s. The requirements for space missions as given in the somewhat dated Advanced Information Processing Systems (AIPS) requirements document are contrasted with the new requirements from JPL/Caltech involving sensor data capture and scene analysis. It is shown that more stringent requirements have arisen as a result of technological advancements. Two possible architectures, the AIPS Proof of Concept (POC) configuration and the MAX Fault-tolerant dataflow multiprocessor, were evaluated. The main observation was that the AIPS design is biased towards fault tolerance and may not be an ideal architecture for planetary and deep space probes due to high cost and complexity. The MAX concepts appears to be a promising candidate, except that more detailed information is required. The feasibility for adding neural computation capability to this architecture needs to be studied. Key impact issues for architectural design of computing systems meant for planetary missions were also identified

Machine Learning and Neural Networks for Real-Time Scheduling

This paper aims to serve as an efficient survey of the processes, problems, and methodologies surrounding the use of Neural Networks, specifically Hopfield-Type, in order to solve Hard-Real-Time Scheduling problems. Our primary goal is to demystify the field of Neural Networks research and properly describe the methods in which Real-Time scheduling problems may be approached when using neural networks. Furthermore, to give an introduction of sorts on this niche topic in a niche field. This survey is derived from four main papers, namely: “A Neurodynamic Approach for Real-Time Scheduling via Maximizing Piecewise Linear Utility” and “Scheduling Multiprocessor Job with Resource and Timing Constraints Using Neural Networks” . “Solving Real Time Scheduling Problems with Hopfield-type Neural Networks” and “Neural Networks for Multiprocessor Real-Time Scheduling

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/

ACCELERATION OF SPIKING NEURAL NETWORKS ON SINGLE-GPU AND MULTI-GPU SYSTEMS

There has been a strong interest in modeling a mammalian brain in order to study the architectural and functional principles of the brain and offer tools to neuroscientists and medical researchers for related studies. Artificial Neural Networks (ANNs) are compute models that try to simulate the structure and/or the functional behavior of neurons and process information using the connectionist approach to computation. Hence, the ANNs are the viable options for such studies. Of many classes of ANNs, Spiking Neuron Network models (SNNs) have been employed to simulate mammalian brain, capturing its functionality and inference capabilities. In this class of neuron models, some of the biologically accurate models are the Hodgkin Huxley (HH) model, Morris Lecar (ML) model, Wilson model, and the Izhikevich model. The HH model is the oldest, most biologically accurate and the most compute intensive of the listed models. The Izhikevich model, a more recent development, is sufficiently accurate and involves the least computations. Accurate modeling of the neurons calls for compute intensive models and hence single core processors are not suitable for large scale SNN simulations due to their serial computation and low memory bandwidth. Graphical Processing Units have been used for general purpose computing as they offer raw computing power, with a majority of logic solely dedicated for computing purpose. The work presented in this thesis implements two-level character recognition networks using the four previously mentioned SNN models in Nvidia\u27s Tesla C870 card and investigates performance improvements over the equivalent software implementation on a 2.66 GHz Intel Core 2 Quad. The work probes some of the important parameters such as the kernel time, memory transfer time and flops offered by the GPU device for the implementations. In this work, we report speed-ups as high as 576x on a single GPU device for the most compute-intensive, highly biologically realistic Hodgkin Huxley model. These results demonstrate the potential of GPUs for large-scale, accurate modeling of the mammalian brain. The research in this thesis also presents several optimization techniques and strategies, and discusses the major bottlenecks that must be avoided in order to achieve maximum performance benefits for applications involving complex computations. The research also investigates an initial multi-GPU implementation to study the problem partitioning for simulating biological-scale neuron networks on a cluster of GPU devices