This paper presents accurate area and power estimation models for implementations using FPGAs from the Xilinx Virtex-2Pro family. These models are designed to facilitate efficient design space exploration in an automated algorithm-architecture codesign framework. Detailed models for accurately estimating the number of slices, block RAMs and 18x18-bit multipliers for fixed point and floating-point IP cores have been developed. These models are also utilized to develop accurate power models that consider the effect of logic power, signal power, clock power and I/O power. In all cases, the model coefficients have been derived by using curve fitting or regression analysis. The modeling error for the IP cores is very small (average 0.95%). The error for fairly large examples such as floating point implementation of 8-point FFTs is also quite small; it is 1.87 % for estimation of number of slices and 3.48 % for estimation of power consumption.

Abstract—This paper describes TANOR, an automated framework for designing hardware accelerators for numerical computation on reconfigurable platforms. Applications utilizing numerical algorithms on large-size data sets require high-throughput computation platforms. The focus is on N-body interaction problems which have a wide range of applications spanning from astrophysics to molecular dynamics. The TANOR design flow starts with a MATLAB description of a particular interaction function, its parameters, and certain architectural constraints specified through a graphical user interface. Subsequently, TANOR automatically generates a configuration bitstream for a target FPGA along with associated drivers and control software necessary to direct the application from a host PC. Architectural exploration is facilitated through support for fully custom fixed-point and floating point representations in addition to standard number representations such as single precision floating point. Moreover, TANOR enables joint exploration of algorithmic and architectural variations in realizing efficient hardware accelerators. TANOR’s capabilities have been demonstrated for three different N-body interaction applications: the calculation of gravitational potential in astrophysics, the diffusion or convolution with Gaussian kernel common in image processing applications, and the force calculation with vector-valued kernel function in molecular dynamics simulation. Experimental results show that TANOR-generated hardware accelerators achieve lower resource utilization without compromising numerical accuracy, in comparison to other existing custom accelerators. Index Terms—Algorithms implemented in hardware, Reconfigurable hardware, Signal processing systems, Numerical algorithms. F 1

Abstract Machine-learning algorithms are employed in a wide variety of applications to extract useful information from data sets, and many are known to suffer from superlinear increases in computational time with increasing data size and number of signals being processed (data dimension). Certain principal machine-learning algorithms are commonly found embedded in larger detection, estimation, or classification operations. Three such principal algorithms are the Parzen window-based, non-parametric estimation of Probability Density Functions (PDFs), K-means clustering and correlation. Because they form an integral part of numerous machine-learning applications, fast and efficient execution of these algorithms is extremely desirable. FPGA-based reconfigurable computing (RC) has been successfully used to accelerate computationally intensive problems in a wide variety of scientific domains to achieve speedup over traditional software implementations. However, this potential benefit is quite often not fully realized because creating efficient FPGA designs is generally carried out in a laborious, case-specific manner requiring a great amount of

Abstract — FPGAs (Field Programmable Gate Arrays) and the field of “High Performance Computing ” have been applied to computationally intensive problems in various domains mainly addressing speedup issues. However, there is still a significant need of in-depth research and proof of success with real applications for proposing them as solutions for a more general class of problems. Along this line, this work involves the design, development and analysis of a multi-core Probability Density Function (PDF) estimation algorithm using Gaussian kernels on FPGAs. Speedup and performance metrics were obtained and discussions addressing scalability issues suggest that FPGAs are a good choice for a wide class of applications that are primarily based on density function estimations.

Approval of the thesis:

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