A preliminary investigation of a neocortex model implementation on the Cray XD1

Rice, Kenneth; Vutsinas, Christopher N.; Taha, Tarek M.

doi:10.1145/1362622.1362626

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…In [41]- [43], hardware chips were used to simulate V1. In [44], another supercomputing-based method was proposed. In [45]- [49], some V1 mechanisms were simulated and used for invariance recognition for some special purposes.…”

Section: B Related Workmentioning

confidence: 99%

Learning and Representing Object Shape Through an Array of Orientation Columns

Wei

Zheng

2014

IEEE Trans. Neural Netw. Learning Syst.

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Recognizing an object from its background is always a very challenging task for pattern recognition, especially when the size, pose, or illumination of the object or the background are changing. The most essential method of handling this classical problem is to learn and define the structure of an object using its topological or geometrical features and components. To create a data structure that can describe the spatial relationships of object components formally and join knowledge learning and applying in a seamless loop, a representation platform must be developed. This platform can serve as a shared workspace not only for learning but also for recognition. In this paper, the platform is established by simulating the primary functional modules in the biological primary visual cortex (V1). V1 is located at the middle level of the visual information processing system. As the conjunction of low-level data and high-level knowledge, it performs visual processing for general purposes. Orientation columns in V1 are simulated in our platform, and an array of such columns is designed to represent the orientation features of edges in an image. With this platform, formalized prototypes are designed to represent each typical view of an object and thus the object concept. Data-and concept-driven processing can shift iteratively on this platform. The processes of acquiring knowledge of an object and applying that knowledge coincide with each other perfectly. The experimental results show that our algorithm can learn from a small training set and can recognize the same types of objects in natural background without any preliminary information. This bioinspired representation platform offers a promising prospect for the handling of semantic-concerned problems that need prior knowledge.

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Section: B Related Workmentioning

confidence: 99%

Learning and Representing Object Shape Through an Array of Orientation Columns

Wei

Zheng

2014

IEEE Trans. Neural Netw. Learning Syst.

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“…informatik.uni-tuebingen.de/software/JavaNNS/welcome_e.html) has seen some use, although these applications are waning with the emergence of spiking networks. Finally, many users use Matlab [23] or C/C++ [52] to write their own neural simulators.…”

Section: Pure Software Simulationmentioning

confidence: 99%

Managing Burstiness and Scalability in Event-Driven Models on the SpiNNaker Neuromimetic System

Rast

Navaridas

Jin

et al. 2011

Int J Parallel Prog

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Neural networks present a fundamentally different model of computation from the conventional sequential digital model, for which conventional hardware is typically poorly matched. However, a combination of model and scalability limitations has meant that neither dedicated neural chips nor FPGA's have offered an entirely satisfactory solution. SpiNNaker introduces a different approach, the "neuromimetic" architecture, that maintains the neural optimisation of dedicated chips while offering FPGA-like universal configurability. This parallel multiprocessor employs an asynchronous event-driven model that uses interrupt-generating dedicated hardware on the chip to support real-time neural simulation. Nonetheless, event handling, particularly packet servicing, requires careful and innovative design in order to avoid local processor congestion and possible deadlock. We explore the impact that spatial locality, temporal causality and burstiness of traffic have on network performance, using tunable, biologically similar synthetic traffic patterns. Having established the viability of the system for real-time operation, we use two exemplar neural models to illustrate how to implement efficient event-handling service routines that mitigate the problem of burstiness in the traffic. Extending work published in ACM Computing Frontiers 2010 with on-chip testing, simulation results indicate the viability of SpiNNaker for large-scale neural modelling, while emphasizing the need for effective burst management and network mapping. Ultimately, the goal is the creation of a library-based development system that can translate a high-level neural model from any description 123Int J Parallel Prog environment into an efficient SpiNNaker instantiation. The complete system represents a general-purpose platform that can generate an arbitrary neural network and run it with hardware speed and scale.

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Scaling analysis of a neocortex inspired cognitive model on the Cray XD1

2008

Self Cite

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This paper presents the implementation and scaling of a neocortex inspired cognitive model on a Cray XD1. Both software and reconfigurable logic based FPGA implementations of the model are examined. This model belongs to a new class of biologically inspired cognitive models. Large scale versions of these models have the potential for significantly stronger inference capabilities than current conventional computing systems. These models have large amounts of parallelism and simple computations, thus allowing highly efficient hardware implementations. As a result, hardware-acceleration of these models can produce significant speedups over fully software implementations. Parallel software and hardware-accelerated implementations of such a model are investigated for networks of varying complexity. A scaling analysis of these networks is presented and utilized to estimate the throughput of both hardware-accelerated and software implementations of larger networks that utilize the full resources of the Cray XD1. Our results indicate that hardware-acceleration can provide average throughput gains of 75 times over software-only implementations of the networks we examined on this system.

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A preliminary investigation of a neocortex model implementation on the Cray XD1

Cited by 4 publications

References 8 publications

Learning and Representing Object Shape Through an Array of Orientation Columns

Learning and Representing Object Shape Through an Array of Orientation Columns

Managing Burstiness and Scalability in Event-Driven Models on the SpiNNaker Neuromimetic System

Scaling analysis of a neocortex inspired cognitive model on the Cray XD1

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