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 our approach on a Cray XD1 and compare the performance potential of scaling the design utilizing reconfigurable logic based acceleration to a software only design. Our results indicate that acceleration using reconfigurable logic can provide a significant speedup over a software only implementation
Humans outperform computers on many natural tasks including vision. Given the human ability to recog...
Visual analytics applications are becoming ubiquitous and embedded in various systems that we intera...
NoAn area-efficient hardware architecture is used to map fully parallel cortical columns on Field Pr...
As the technologies continue to evolve, our computers have more and more computing capacity, which d...
Even though computing systems have increased the number of transistors, the switching speed, and the...
The paper describes the architecture and the simulated performances of a memory-based chip that emul...
The highly cross-disciplinary emerging field of neuromorphic computing architectures for cognitive i...
The performance potential of future architectures, thanks to Moores Law, grows linearly with the nu...
The spread of graphics processing unit (GPU) computing paved the way to the possibility of reaching ...
Neuromorphic engineering pursues the design of electronic systems emulating function and structural ...
For the first time in decades computers are evolving into a fundamentally new class of machine. Tran...
Despite the great strides neuroscience has made in recent decades, the underlying principles of brai...
Lachmair J, Merényi E, Porrmann M, Rückert U. A reconfigurable neuroprocessor for self-organizing fe...
Abstract—These days, many traditional end-user applications are said to “run fast enough ” on existi...
An area-efficient hardware architecture is used to map fully parallel cortical columns on Field Prog...
Humans outperform computers on many natural tasks including vision. Given the human ability to recog...
Visual analytics applications are becoming ubiquitous and embedded in various systems that we intera...
NoAn area-efficient hardware architecture is used to map fully parallel cortical columns on Field Pr...
As the technologies continue to evolve, our computers have more and more computing capacity, which d...
Even though computing systems have increased the number of transistors, the switching speed, and the...
The paper describes the architecture and the simulated performances of a memory-based chip that emul...
The highly cross-disciplinary emerging field of neuromorphic computing architectures for cognitive i...
The performance potential of future architectures, thanks to Moores Law, grows linearly with the nu...
The spread of graphics processing unit (GPU) computing paved the way to the possibility of reaching ...
Neuromorphic engineering pursues the design of electronic systems emulating function and structural ...
For the first time in decades computers are evolving into a fundamentally new class of machine. Tran...
Despite the great strides neuroscience has made in recent decades, the underlying principles of brai...
Lachmair J, Merényi E, Porrmann M, Rückert U. A reconfigurable neuroprocessor for self-organizing fe...
Abstract—These days, many traditional end-user applications are said to “run fast enough ” on existi...
An area-efficient hardware architecture is used to map fully parallel cortical columns on Field Prog...
Humans outperform computers on many natural tasks including vision. Given the human ability to recog...
Visual analytics applications are becoming ubiquitous and embedded in various systems that we intera...
NoAn area-efficient hardware architecture is used to map fully parallel cortical columns on Field Pr...