Energy-efficient algebra kernels in FPGA for High Performance Computing

High-Performance Linear Algebra Processor using FPGA

With recent advances in FPGA (Field Programmable Gate Array) technology it is now feasible to use these devices to build special purpose processors for floating point intensive applications that arise in scientific computing. FPGA provides programmable hardware that can be used to design custom hardware without the high-cost of traditional hardware design. In this talk we discuss two multi-proc...

Energy-efficient Resource Management for High-performance Computing Platforms

PAM-Blox: High Performance FPGA Design for Adaptive Computing

PAM-Blox are object-oriented circuit generators on top of the PCI Pamette design environment, PamDC. Highperformance FPGA design for adaptive computing is simplified by using a hierarchy of optimized hardware objects described in C++. PAM-Blox consist of two major layers of abstraction. First, PamBlox are parameterizable simple elements such as counters and adders. Automatic placement of carry ...

IANUS: an FPGA-based System for High Performance Scientific Computing

This paper describes IANUS, a modular massively parallel and reconfigurable FPGA-based computing system. Each IANUS module has a computational core and a host. The computational core is a 4x4 array of FPGA-based processing elements with nearest-neighbor data links. Processors are also directly connected to an I/O node attached to the IANUS host, a conventional PC. IANUS is tailored for, but not...

Energy-Efficient FPGA-Based Parallel Quasi-Stochastic Computing

The high performance of FPGA (Field Programmable Gate Array) in image processing applications is justified by its flexible reconfigurability, its inherent parallel nature and the availability of a large amount of internal memories. Lately, the Stochastic Computing (SC) paradigm has been found to be significantly advantageous in certain application domains including image processing because of i...