Abstract-An automated static approach for optimizing bit widths of fixed-point feedforward designs with guaranteed accuracy, called MiniBit, is presented. Methods to minimize both the integer and fraction parts of fixed-point signals with the aim of minimizing the circuit area are described. For range analysis, the technique in this paper identifies the number of integer bits necessary to meet range requirements. For precision analysis, a semianalytical approach with analytical error models in conjunction with adaptive simulated annealing is employed to optimize the number of fraction bits. The analytical models make it possible to guarantee overflow/underflow protection and numerical accuracy for all inputs over the user-specified input intervals. Using a stream compiler for field-programmable gate arrays (FPGAs), the approach in this paper is demonstrated with polynomial approximation, RGB-to-YCbCr conversion, matrix multiplication, B-splines, and discrete cosine transform placed and routed on a Xilinx Virtex-4 FPGA. Improvements for a given design reduce the area and the latency by up to 26% and 12%, respectively, over a design using optimum uniform fraction bit widths. Studies show that MiniBit-optimized designs are within 1% of the area produced from the integer linear programming approach.Index Terms-Field-programmable gate arrays (FPGAs), finite word-length effects, fixed-point arithmetic, optimization methods, simulated annealing (SA).
Reconfigurable computing is becoming increasingly attractive for many applications. This survey covers two aspects of reconfigurable computing: architectures and design methods. The paper includes recent advances in reconfigurable architectures, such as the Alters Stratix II and Xilinx Virtex 4 FPGA devices. The authors identify major trends in general-purpose and specialpurpose design methods. It is shown that reconfigurable computing designs are capable of achieving up to 500 times speedup and 70% energy savings over microprocessor implementations for specific applications.
Automatic generation of custom instruction processors from high-level application descriptions enables fast design space exploration, while offering very favorable performance and silicon area combinations. This work introduces a novel method for adapting the instruction set to match an application captured in a high-level language. A simplified model is used to find the optimal instructions via enumeration of maximal convex subgraphs of application data flow graphs (DFGs). Our experiments involving a set of multi-
PAM-Blox are object-oriented circuit generators on top of the PCI Pamette design environment, PamDC. High-performance 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 chains and flexible shapes are supported. PaModules are more complex elements possibly instantiating PamBlox. PaModules have fixed shapes and are usually optimized for a specific data-width. Examples for PaModules are multipliers, Coordinate Rotations (CORDICs), and special arithmetic units for encryption.The key difference of our approach to most other design tools for FPGAs is that the designer has total control over placement at each level of the design hierarchy, which is the key to high-performance FPGA design. Second, the object interface was chosen carefully to encourage code-reuse and simplify code-sharing between designers.PAM-Blox are intended to be part of an open library that allows design sharing between members of the adaptive computing community.
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