We present a performance analysis framework that efficiently generates and analyzes hardware designs for computationally intensive signal processing applications. Our framework synthesizes designs from a high level of abstraction into well-constructed and recognizable hardware structures that perform well in terms of area, throughput and power dissipation. Cost functions provided by our framework allow the user to reduce the design space to a set of efficient hardware implementations that meet performance constraints. We utilize our framework to estimate hardware performance using a set of pre-synthesized mathematical cores which expedites the synthesis process by approximately 14 fold. This reduces the architectural generation and hardware synthesis process from days to several hours for complex designs. Our work aims at performing hardware optimizations at the architectural and arithmetic levels, relieving the user from manually describing the designs at the register transfer level and iteratively varying the hardware structures. We illustrate the efficiency and accuracy of our framework by generating finite impulse response filter structures used in several signal processing applications such as adaptive equalizers and quadrature mirror filters. The results show that hardware filter structures generated by our framework can achieve, on average, a 3 fold increase in power efficiency when compared to manually constructed designs.

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This paper presents a design environment for efficiently generating application-specific Intellectual Property (IP) cores for system level signal processing algorithms. We present our view of a framework that combines common electronic design automation (EDA) tools to alleviate the designer from manually constructing the hardware models and analyzing their performance. We use our framework to efficiently implement design optimizations that improve the performance of the overall hardware architectures. Our framework is well suited for designers with a range of signal processing and hardware expertise. Our framework generates the dedicated IP cores and estimates the performance such as area, critical path delay, and latency within seconds. Parts of our framework also compare different hardware designs for various digital signal processing (DSP) algorithms and allows the designer to make architectural decisions earlier in the hardware design process. We use a GUI-based framework invoked from MATLAB to automatically build and analyze the hardware designs. Our framework generates efficient hardware designs described in SystemC and Verilog code, along with the performance metrics for each architecture. We illustrate the use of our framework by exploring and analyzing architectural variations of two case studies: finite impulse response (FIR) filters and adaptive channel equalizers.

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This paper presents a methodology for implementing infinite impulse response (IIR) filters in hardware. The methodology we explore utilizes concepts inherent to both second order sections (SOS) and state-space models. We illustrate an improvement of several orders of magnitude in computational accuracy using the state-space SOS IIR filters over the cascade designs. Additionally, the state-space hardware model exhibits a shorter critical path when mapped onto a programmable device. We compare the performance of the state-space hardware model to conventional IIR filter implementations using a Spartan 3 XC3S400-4FG320 FPGA to illustrate the merits of this work.