Low Power Montgomery Modular Multiplication on Reconfigurable Systems

This paper presents an area-optimized FPGA architecture of the Montgomery modular multiplication algorithm on a low power reconfigurable IGLOO® 2 FPGA of Microsemi®. Our contributions consist of the mapping of the Montgomery algorithm to the specific architecture of the target FPGA, using the pipelined Math blocks and the embedded memory blocks. We minimize the occupation of these blocks as well as the usage of the regular FPGA cells (LUT4 and Flip Flops) through an dedicated scheduling algorithm. The obtained results suggest that a 224-bit modular multiplication can be computed in 2.42 µs, at a cost of 444 LUT4, 160 Flip Flops, 1 Math Block and 1 64x18 RAM, with a power consumption of 25.35 mW. If more area resources are considered, modular multiplication can be performed in 1.30 µs at a cost of 658 LUT4, 268 Flip Flops, 2 Math Blocks, 2 64x18 RAMs and a power consumption of 36.02 mW

The Design Space of Ultra-low Energy Asymmetric Cryptography

The energy cost of asymmetric cryptography, a vital component of modern secure communications, inhibits its wide spread adoption within the ultra-low energy regimes such as Implantable Medical Devices (IMDs), Wireless Sensor Networks (WSNs), and Radio Frequency Identification tags (RFIDs). In literature, a plethora of hardware and software acceleration techniques exists for improving the performance of asymmetric cryptography. However, very little attention has been focused on the energy efficiency. Therefore, in this dissertation, I explore the design space thoroughly, evaluating proposed hardware acceleration techniques in terms of energy cost and showing how effective they are at reducing the energy per cryptographic operation. To do so, I estimate the energy consumption for six different hardware/software configurations across five levels of security, including both GF(p) and GF(2^m) computation. First, we design and evaluate an efficient baseline architecture for pure software-based cryptography, which is centered around a pipelined RISC processor with 256KB of program ROM and 16KB of RAM. Then, we augment our processor design with simple, yet beneficial instruction set extensions for GF(p) computation and evaluate the improvement in terms of energy per cryptographic operation compared to the baseline microarchitecture. While examining the energy breakdown of the system, it became clear that fetching instructions from program memory was contributing significantly to the overall energy consumption. Thus, we implement a parameterizable instruction cache and simulate various configurations. We determine that for our working set, the energy-optimal instruction cache is 4KB, providing a 25% energy improvement over the baseline architecture for a 192-bit key-size. Next, we introduce a reconfigurable GF(p) accelerator to our microarchitecture and mea sure the energy per operation against the baseline and the ISA extensions. For ISA extensions, we show between 1.32 and 1.45 factor improvement in energy efficiency over baseline, while for full acceleration we demonstrate a 5.17 to 6.34 factor improvement. Continuing towards greater efficiency, we investigate the energy efficiency of different arithmetic by first adding GF(2^m) instruction set extensions to our processor architecture and comparing them to their GF(p) counterpart. Finally, we design a non-configurable 163-bit GF(2^m) accelerator and perform some initial energy estimates, comparing them with our prior work. In the end, we discuss our ongoing research and make suggestions for future work. The work presented here, along with proposed future work, will aid in bringing asymmetric cryptography within reach of ultra-low energy devices

Applications in Electronics Pervading Industry, Environment and Society

This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp