Parametric, Secure and Compact Implementation of RSA on FPGA

We present a fast, efficient, and parameterized modular multiplier and a secure exponentiation circuit especially intended for FPGAs on the low end of the price range. The design utilizes dedicated block multipliers as the main functional unit and Block-RAM as storage unit for the operands. The adopted design methodology allows adjusting the number of multipliers, the radix used in the multipliers, and number of words to meet the system requirements such as available resources, precision and timing constraints. The architecture, based on the Montgomery modular multiplication algorithm, utilizes a pipelining technique that allows concurrent operation of hardwired multipliers. Our design completes 1020-bit and 2040-bit modular multiplications in 7.62 μs and 27.0 μs, respectively. The multiplier uses a moderate amount of system resources while achieving the best area-time product in literature. 2040-bit modular exponentiation engine can easily fit into Xilinx Spartan-3E 500; moreover the exponentiation circuit withstands known side channel attacks

Transformations of High-Level Synthesis Codes for High-Performance Computing

Specialized hardware architectures promise a major step in performance and energy efficiency over the traditional load/store devices currently employed in large scale computing systems. The adoption of high-level synthesis (HLS) from languages such as C/C++ and OpenCL has greatly increased programmer productivity when designing for such platforms. While this has enabled a wider audience to target specialized hardware, the optimization principles known from traditional software design are no longer sufficient to implement high-performance codes. Fast and efficient codes for reconfigurable platforms are thus still challenging to design. To alleviate this, we present a set of optimizing transformations for HLS, targeting scalable and efficient architectures for high-performance computing (HPC) applications. Our work provides a toolbox for developers, where we systematically identify classes of transformations, the characteristics of their effect on the HLS code and the resulting hardware (e.g., increases data reuse or resource consumption), and the objectives that each transformation can target (e.g., resolve interface contention, or increase parallelism). We show how these can be used to efficiently exploit pipelining, on-chip distributed fast memory, and on-chip streaming dataflow, allowing for massively parallel architectures. To quantify the effect of our transformations, we use them to optimize a set of throughput-oriented FPGA kernels, demonstrating that our enhancements are sufficient to scale up parallelism within the hardware constraints. With the transformations covered, we hope to establish a common framework for performance engineers, compiler developers, and hardware developers, to tap into the performance potential offered by specialized hardware architectures using HLS

High throughput spatial convolution filters on FPGAs

Digital signal processing (DSP) on field- programmable gate arrays (FPGAs) has long been appealing because of the inherent parallelism in these computations that can be easily exploited to accelerate such algorithms. FPGAs have evolved significantly to further enhance the mapping of these algorithms, included additional hard blocks, such as the DSP blocks found in modern FPGAs. Although these DSP blocks can offer more efficient mapping of DSP computations, they are primarily designed for 1-D filter structures. We present a study on spatial convolutional filter implementations on FPGAs, optimizing around the structure of the DSP blocks to offer high throughput while maintaining the coefficient flexibility that other published architectures usually sacrifice. We show that it is possible to implement large filters for large 4K resolution image frames at frame rates of 30–60 FPS, while maintaining functional flexibility