A CNN framework for modeling parallel processing in a mammalian retina: Research Articles
International Journal of Circuit Theory and Applications - CNN Technology
FCCM '06 Proceedings of the 14th Annual IEEE Symposium on Field-Programmable Custom Computing Machines
A Platform-Based Taxonomy for ESL Design
IEEE Design & Test
Families of FPGA-based accelerators for approximate string matching
Microprocessors & Microsystems
High Performance Implementation of an FPGA-Based Sequential DT-CNN
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
| Hi-index | 0.01 |
The complexity of hardware design methodologies represents a significant difficulty for non-hardware focused scientists working on accelerating the simulation of complex bio-inspired applications. An emerging generation of electronic system level (ESL) design tools is been developed, which allow software-hardware codesign and partitioning of complex algorithms from high level language (HLL) descriptions. These tools, together with high performance reconfigurable computer (HPRC) systems consisting of standard microprocessors coupled with application specific FPGA chips, provide a new approach for rapid emulation and acceleration of highly parallelizable algorithms. In this article CoDeveloper, and ESL IDE from Impulse Accelerated Technologies, are analyzed. A model for the first synapse of the retina, based on a discrete-time sequential CNN architecture suitable for FPGA implementation proposed by the authors in a previous paper, is implemented using CoDeveloper tools and the DS1002 HPRC platform from DRC Computers. Results showed that, with a minimum development time, a 10xacceleration, when compared to the software emulation, can be obtained.