Kenneth L. Rice

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There has been a strong push recently to examine biological scale simulations of neuromorphic algorithms to achieve stronger inference capabilities than current computing algorithms. The recent Izhikevich spiking neuron model is ideally suited for such large scale cortical simulations due to its efficiency and biological accuracy. In this paper we explore(More)
We present 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(More)
In this paper we study the acceleration of a new class of cognitive processing applications based on the structure of the neocortex. Specifically we examine the speedup of a visual cortex model for image recognition. We propose techniques to accelerate the application on general purpose processors and on reconfigurable logic. We present implementations of(More)
In this paper we study the acceleration of a new class of cognitive processing applications based on the structure of the neocortex. Our focus is on a model of the visual cortex used for image recognition developed by George and Hawkins. We propose techniques to accelerate the algorithm using reconfigurable logic, specifically a streaming memory(More)
At present, a major initiative in the research community is investigating new ways of processing data that capture the efficiency of the human brain in hardware and software. This has resulted in increased interest and development of bio-inspired computing approaches in software and hardware. One such bio-inspired approach is Cellular Simultaneous Recurrent(More)
Keywords: Biologically inspired Neocortex model Reconfigurable logic Streaming memory Context switching a b s t r a c t A novel architecture to accelerate a neocortex inspired cognitive model is presented. The architecture utilizes a collection of context switchable processing elements (PEs). This enables time multiplexing of nodes in the model onto(More)
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