Field programmable gate arrays (FPGAs) are a promising technology for developing high-performance embedded systems. The density and performance of FPGAs have drastically improved over the past few years. Consequently, the size of the configuration bit-streams has also increased considerably. As a result, the cost-effectiveness of FPGA-based embedded systems is significantly affected by the memory required for storing various FPGA configurations. This paper proposes a novel compression technique that reduces the memory required for storing FPGA configurations and results in high decompression efficiency. Decompression efficiency corresponds to the decompression hardware cost as well as the decompression rate. The proposed technique is applicable to any SRAM-based FPGA device since configuration bit-streams are processed as raw data. The required decompression hardware is simple and the decompression rate scales with the speed of the memory used for storing the configuration bit-streams. Moreover, the time to configure the device is not affected by our compression technique. Using our technique, we demonstrate up to 41% savings in memory for configuration bit-streams of several real-world applications.

Architectures that implement the Internet Protocol Security (IPSec) standard have to meet the enormous computing demands of cryptographic algorithms. In addition, IPSec architectures have to be flexible enough to adapt to diverse security parameters. This article proposes an FPGA-based Adaptive Cryptographic Engine (ACE) for IPSec architectures. By taking advantage of FPGA technology, ACE can adapt to diverse security parameters on the fly while providing superior performance compared with software-based solutions. In this paper, we focus on performance issues. A diverse set of private-key cryptographic algorithms is utilized to demonstrate the applicability of the proposed cryptographic engine. The time performance metrics are throughput and key-setup latency. The latency metric is the most important measure for IPSec where a small amount of data is processed per key and key context switching occurs repeatedly. We are not aware of any published results that include extensive key-setup latency results.

FPGAs are a promising technology for accelerating SAT solvers. Besides their high density, fine granularity, and massive parallelism, FPGAs provide the opportunity for run-time customization of the hardware based on the given SAT instance. In this article, a parallel deduction engine is proposed for backtrack search algorithms. The performance of the deduction engine is critical to the overall performance of the algorithm because, for any moderate SAT instance, millions of implications are derived. We propose a novel approach in which p , the amount of parallelization of the engine, is fine-tuned during problem solving in order to optimize performance. Not only the hardware is initially customized based on the input instance, but it is also dynamically modified in terms of p based on the knowledge gained during solving the SAT instance. Compared with conventional deduction engines that correspond to p = 1, we demonstrate speedups in the range of 2.87 to 5.44 for several SAT instances.

FPGAs are a promising technology for developing high-performance embedded systems. The density and performance of FPGAs have drastically improved over the past few years. Consequently, the size of the configuration bit-streams has also increased considerably. As a result, the cost-effectiveness of FPGA-based embedded systems is significantly affected by the memory required for storing various FPGA configurations. This paper proposes a novel compression technique that reduces the memory required for storing FPGA configurations and results in high decompression efficiency. Decompression efficiency corresponds to the decompression hardware cost as well as the decompression rate. The proposed technique is applicable to any SRAM-based FPGA device since configuration bit-streams are processed as raw data. The required decompression hardware is simple and is independent of the individual semantics of configuration bit-streams or specific features of the on-chip configuration mechanism. Moreover, the time to configure the device is not affected by our compression technique. Using our technique, we demonstrate up to $41 \%$ savings in memory for configuration bit-streams of several real-world applications.

Architectures that implement the Internet Protocol Security (IPSec) standard have to meet the enormous computing demands of cryptographic algorithms. In addition, IPSec architectures have to be flexible enough to adapt to diverse security parameters. This paper proposes an FPGA-based Adaptive Cryptographic Engine (ACE) for IPSec architectures. By taking advantage of FPGA technology, ACE can adapt to diverse security parameters on the fly while providing superior performance compared with software-based approaches. For example, for the final candidate algorithms of the Advanced Encryption Standard (AES), our techniques lead to throughput speed-up of 4-20 while the key-setup latency time is reduced by a factor of 20-700 compared with software-based approaches. We also develop a compression technique that reduces the memory requirements of ACE without the need for dedicated hardware. Though data compression has been extensively studied before, we are not aware of any prior work that addresses the compression problem of FPGA-based embedded systems with respect to the implementation cost. Using our technique, we demonstrate up to 40% savings in memory for various configuration bit-streams.

FPGAs are fine-grained architectures, mainly designed for implementing bit-level tasks and random logic functions. Their performance is limited for computationally demanding applications over large word length data. A highly promising avenue that is being explored by many research groups is coarse-grained configurable architectures. These architectures are datapath-oriented structures and consist of a small number of powerful, word-based configurable processing elements (PEs). Such architectures can result in greater computational efficiency and high throughput for coarse-grained computing tasks. The key for achieving high performance solutions is efficient mapping of tasks onto above architectures. In addition to achieving high computational rates, partitionability is a desirable characteristic of the mapping. Moreover, the computational efficiency must scale with the size of the architecture. Finally, it must result in a simple PE structure, regular/balanced dataflow and sustainable I/O requirements so that it can be realized in hardware. In this paper we show a methodology for deriving dynamic computation structures for 2 dimensioned homogeneous computations. Homogeneous computations lead to all PEs having the same functionality. The derived dynamic structures match the datapath-oriented nature of coarse-grained architectures and lead to efficient mapping schemes. Our solutions require constant I/O and smaller amount of local memory/PE compared with known solutions.