Neural Simulations on Multi-Core Architectures

Neuroscience is witnessing increasing knowledge about the anatomy and electrophysiological properties of neurons and their connectivity, leading to an ever increasing computational complexity of neural simulations. At the same time, a rather radical change in personal computer technology emerges with the establishment of multi-cores: high-density, explicitly parallel processor architectures for both high performance as well as standard desktop computers. This work introduces strategies for the parallelization of biophysically realistic neural simulations based on the compartmental modeling technique and results of such an implementation, with a strong focus on multi-core architectures and automation, i.e. user-transparent load balancing

Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system

Investigation of LSTM Based Prediction for Dynamic Energy Management in Chip Multiprocessors

In this paper, we investigate the effectiveness of using long short-term memory (LSTM) instead of Kalman filtering to do prediction for the purpose of constructing dynamic energy management (DEM) algorithms in chip multi-processors (CMPs). Either of the two prediction methods is employed to estimate the workload in the next control period for each of the processor cores. These estimates are then used to select voltage-frequency (VF) pairs for each core of the CMP during the next control period as part of a dynamic voltage and frequency scaling (DVFS) technique. The objective of the DVFS technique is to reduce energy consumption under performance constraints that are set by the user. We conduct our investigation using a custom Sniper system simulation framework. Simulation results for 16 and 64 core network-on-chip based CMP architectures and using several benchmarks demonstrate that the LSTM is slightly better than Kalman filtering

Investigation of LSTM Based Prediction for Dynamic Energy Management in Chip Multiprocessors

In this paper, we investigate the effectiveness of using long short-term memory (LSTM) instead of Kalman filtering to do prediction for the purpose of constructing dynamic energy management (DEM) algorithms in chip multi-processors (CMPs). Either of the two prediction methods is employed to estimate the workload in the next control period for each of the processor cores. These estimates are then used to select voltage-frequency (VF) pairs for each core of the CMP during the next control period as part of a dynamic voltage and frequency scaling (DVFS) technique. The objective of the DVFS technique is to reduce energy consumption under performance constraints that are set by the user. We conduct our investigation using a custom Sniper system simulation framework. Simulation results for 16 and 64 core network-on-chip based CMP architectures and using several benchmarks demonstrate that the LSTM is slightly better than Kalman filtering

Analytic Performance Modeling and Analysis of Detailed Neuron Simulations

Big science initiatives are trying to reconstruct and model the brain by attempting to simulate brain tissue at larger scales and with increasingly more biological detail than previously thought possible. The exponential growth of parallel computer performance has been supporting these developments, and at the same time maintainers of neuroscientific simulation code have strived to optimally and efficiently exploit new hardware features. Current state of the art software for the simulation of biological networks has so far been developed using performance engineering practices, but a thorough analysis and modeling of the computational and performance characteristics, especially in the case of morphologically detailed neuron simulations, is lacking. Other computational sciences have successfully used analytic performance engineering and modeling methods to gain insight on the computational properties of simulation kernels, aid developers in performance optimizations and eventually drive co-design efforts, but to our knowledge a model-based performance analysis of neuron simulations has not yet been conducted. We present a detailed study of the shared-memory performance of morphologically detailed neuron simulations based on the Execution-Cache-Memory (ECM) performance model. We demonstrate that this model can deliver accurate predictions of the runtime of almost all the kernels that constitute the neuron models under investigation. The gained insight is used to identify the main governing mechanisms underlying performance bottlenecks in the simulation. The implications of this analysis on the optimization of neural simulation software and eventually co-design of future hardware architectures are discussed. In this sense, our work represents a valuable conceptual and quantitative contribution to understanding the performance properties of biological networks simulations.Comment: 18 pages, 6 figures, 15 table