Partial Hardware Reconfiguration Research Articles

Towards Dynamic and Partial Reconfigurable Hardware Architectures for Cryptographic Algorithms on Embedded Devices

In the era of IoT, embedded systems are becoming the cornerstone of many IoT related applications, such as smart cars and wearable devices. However, embedded devices have numerous constraints and requirements, including stringent area and power, reduced cost and time-to-market, and increased speedup. Furthermore, these applications are becoming increasingly compute/data-intensive requiring more processing power. Also, especially for IoT related applications, security is another major issue in resource-constrained embedded devices. Although cryptographic algorithms are widely used to ensure the security of these applications, commonly used ones, such as AES, are unsuitable for highly constrained embedded devices, due to their sheer complexity. Hence, several lightweight cryptographic algorithms were proposed in the literature that might be better suited for embedded devices. From these, SPECK and SIMON, introduced by NSA, are the two most popular ones. Another important challenge is how to incorporate the cryptographic algorithms in to embedded devices, efficiently and effectively, without compromising the integrity of the compute/data-intensive applications running on these small-footprint devices. Our previous analysis demonstrated that FPGAs are currently the best avenue to support compute/data-intensive applications running on resource-constrained embedded devices, due to FPGA’s many attractive traits, including, post-fabrication reprogrammability, dynamic and partial reconfiguration capabilities, and reduced time-to-market. Also, FPGAs can be utilized to provide several advantages/features required for the embedded device’s security, such as cryptographic algorithm agility, algorithm upload, algorithm modification, and resource efficiency. In this research work, we introduce novel, unique, and efficient dynamic and partial reconfigurable hardware architectures for the most popular SPECK and SIMON algorithms on embedded devices, considering the constraints associated with these devices and the requirements of the applications running on embedded devices. We also introduce unique system-level architectures for our proposed designs. To the best of our knowledge, no similar work exists in the literature that provides dynamic and partial reconfigurable hardware for SPECK and SIMON, and also provides system-level architecture. Our dynamic and partial reconfigurable hardware designs achieve 28% space saving compared to its static reconfigurable hardware, and 59 times speedup compared to its software counterpart.

Dynamic partial reconfigurable hardware architecture for principal component analysis on mobile and embedded devices

With the advancement of mobile and embedded devices, many applications such as data mining have found their way into these devices. These devices consist of various design constraints including stringent area and power limitations, high speed-performance, reduced cost, and time-to-market requirements. Also, applications running on mobile devices are becoming more complex requiring significant processing power. Our previous analysis illustrated that FPGA-based dynamic reconfigurable systems are currently the best avenue to overcome these challenges. In this research work, we introduce efficient reconfigurable hardware architecture for principal component analysis (PCA), a widely used dimensionality reduction technique in data mining. For mobile applications such as signature verification and handwritten analysis, PCA is applied initially to reduce the dimensionality of the data, followed by similarity measure. Experiments are performed, using a handwritten analysis application together with a benchmark dataset, to evaluate and illustrate the feasibility, efficiency, and flexibility of reconfigurable hardware for data mining applications. Our hardware designs are generic, parameterized, and scalable. Furthermore, our partial and dynamic reconfigurable hardware design achieved 79 times speedup compared to its software counterpart, and 71% space saving compared to its static reconfigurable hardware design.

Dynamic power optimization by exploiting self-reconfiguration in Xilinx Spartan 3-based systems

Reconfigurable architectures are increasingly often applied in various industrial data processing applications, due to the possibility for performing parallel computations and achieving a simplified Systemon-Chip design flow. Furthermore, the exploitation of dynamic and partial hardware reconfiguration has been investigated in different research projects, often in systems based on Xilinx Virtex 2/4 FPGA families, by time-multiplexing hardware resources for multiple functions. This paper describes the exploitation of partial reconfiguration for dynamic power management in a low-power Spartan 3-based level measurement application. The reconfiguration process is thereby applied to optimize system implementation according to the applications requirements on power consumption and performance.