Implementing Backpropagation Training on a Recon gurable Computer Using Pipelining of the Training Patterns

RRANN: A Hardware Implementation of the Backpropagation Algorithm Using Recon gurable FPGAs

Recon gurable Computing: Architectures, Models and Algorithms

The performance requirements of applications have continuously superseded the computing power of architecture platforms. Increasingly larger number of transistors available on a chip have resulted in complex architectures and integration of various architecture components on the chip. But, the incremental performance gains obtained are lower as the complexity and the integration increase. Recon...

Reconngurable Computing: Architectures, Models and Algorithms Current Science: Special Section on Computational Science

ion Mapping Parameters Scheduling Figure 2: Traditional Design Synthesis Approach and the Model-based Approach One major problem in using FPGAs to speed-up a computation is the design process. The \standard CAD approach" used for digital design is typically employed (see Figure 2). The required functionality is speci ed at a high level of abstraction via an HDL or a schematic. FPGA libraries sp...

Constant-Time Hough Transform on a 3D Reconfigurable Mesh Using Fewer Processors

The Hough transform has many applications in image processing and computer vision, including line detection, shape recognition and range alignment for moving imaging objects. Many constant-time algorithms for computing the Hough transform have been proposed on recon gurable meshes [1, 5, 6, 7, 9, 10]. Among them, the ones described in [1, 10] are the most e cient. For a problem with an N N imag...

Hardware Recon gurable Neural Networks

This paper presents the concept of recon gurable systems using the example of a digital hardware implementation of neural networks, as well as RENCO, a platform very well-suited for the prototyping of such systems. RENCO is a network computer containing a recon gurable part composed of four Flex10K FPGAs from Altera (10K130 or 10K250).