Efficient Implementation on Low-Cost SoC-FPGAs of TLSv1.2 Protocol with ECC_AES Support for Secure IoT Coordinators

Security management for IoT applications is a critical research field, especially when taking into account the performance variation over the very different IoT devices. In this paper, we present high-performance client/server coordinators on low-cost SoC-FPGA devices for secure IoT data collection. Security is ensured by using the Transport Layer Security (TLS) protocol based on the TLS_ECDHE_ECDSA_WITH_AES_128_CBC_SHA256 cipher suite. The hardware architecture of the proposed coordinators is based on SW/HW co-design, implementing within the hardware accelerator core Elliptic Curve Scalar Multiplication (ECSM), which is the core operation of Elliptic Curve Cryptosystems (ECC). Meanwhile, the control of the overall TLS scheme is performed in software by an ARM Cortex-A9 microprocessor. In fact, the implementation of the ECC accelerator core around an ARM microprocessor allows not only the improvement of ECSM execution but also the performance enhancement of the overall cryptosystem. The integration of the ARM processor enables to exploit the possibility of embedded Linux features for high system flexibility. As a result, the proposed ECC accelerator requires limited area, with only 3395 LUTs on the Zynq device used to perform high-speed, 233-bit ECSMs in 413 µs, with a 50 MHz clock. Moreover, the generation of a 384-bit TLS handshake secret key between client and server coordinators requires 67.5 ms on a low cost Zynq 7Z007S device

A field programmable gate array based modular motion control platform

The expectations from motion control systems have been rising day by day. As the systems become more complex, conventional motion control systems can not achieve to meet all the specifications with optimized results. This creates the necessity of fundamental changes in the infrastructure of the system. Field programmable gate array (FPGA) technology enables the reconfiguration of the digital hardware, thus dissolving the necessity of infrastructural changes for minor manipulations in the hardware even if the system is deployed. An FPGA based hardware system shrinks the size of the hardware hence the cost. FPGAs also provide better power ratings for the systems as well as a more reliable system with improved performance. As a trade off, the development is rather more difficult than software based systems, which also affects the research and development time of the overall system. In this paper a level of abstraction is introduced in order to diminish the requirement of advanced hardware description language (HDL) knowledge for implementing motion control systems thoroughly on an FPGA. The intellectual property library consists of synthesizable hardware modules specifically implemented for motion control purposes. Other parts of a motion control system, like user interface and trajectory generation, are implemented as software functions in order to protect the modularity of the system. There are also several external hardware designs for interfacing and driving various types of actuators

A general framework for efficient FPGA implementation of matrix product

Original article can be found at: http://www.medjcn.com/ Copyright Softmotor LimitedHigh performance systems are required by the developers for fast processing of computationally intensive applications. Reconfigurable hardware devices in the form of Filed-Programmable Gate Arrays (FPGAs) have been proposed as viable system building blocks in the construction of high performance systems at an economical price. Given the importance and the use of matrix algorithms in scientific computing applications, they seem ideal candidates to harness and exploit the advantages offered by FPGAs. In this paper, a system for matrix algorithm cores generation is described. The system provides a catalog of efficient user-customizable cores, designed for FPGA implementation, ranging in three different matrix algorithm categories: (i) matrix operations, (ii) matrix transforms and (iii) matrix decomposition. The generated core can be either a general purpose or a specific application core. The methodology used in the design and implementation of two specific image processing application cores is presented. The first core is a fully pipelined matrix multiplier for colour space conversion based on distributed arithmetic principles while the second one is a parallel floating-point matrix multiplier designed for 3D affine transformations.Peer reviewe

Customisable arithmetic hardware designs

Imperial Users onl

Application-Specific Number Representation

Reconfigurable devices, such as Field Programmable Gate Arrays (FPGAs), enable application- specific number representations. Well-known number formats include fixed-point, floating- point, logarithmic number system (LNS), and residue number system (RNS). Such different number representations lead to different arithmetic designs and error behaviours, thus produc- ing implementations with different performance, accuracy, and cost. To investigate the design options in number representations, the first part of this thesis presents a platform that enables automated exploration of the number representation design space. The second part of the thesis shows case studies that optimise the designs for area, latency or throughput from the perspective of number representations. Automated design space exploration in the first part addresses the following two major issues: ² Automation requires arithmetic unit generation. This thesis provides optimised arithmetic library generators for logarithmic and residue arithmetic units, which support a wide range of bit widths and achieve significant improvement over previous designs. ² Generation of arithmetic units requires specifying the bit widths for each variable. This thesis describes an automatic bit-width optimisation tool called R-Tool, which combines dynamic and static analysis methods, and supports different number systems (fixed-point, floating-point, and LNS numbers). Putting it all together, the second part explores the effects of application-specific number representation on practical benchmarks, such as radiative Monte Carlo simulation, and seismic imaging computations. Experimental results show that customising the number representations brings benefits to hardware implementations: by selecting a more appropriate number format, we can reduce the area cost by up to 73.5% and improve the throughput by 14.2% to 34.1%; by performing the bit-width optimisation, we can further reduce the area cost by 9.7% to 17.3%. On the performance side, hardware implementations with customised number formats achieve 5 to potentially over 40 times speedup over software implementations

Accelerating LTV based homomorphic encryption in reconfigurable hardware

After being introduced in 2009, the first fully homomorphic encryption (FHE) scheme has created significant excitement in academia and industry. Despite rapid advances in the last 6 years, FHE schemes are still not ready for deployment due to an efficiency bottleneck. Here we introduce a custom hardware accelerator optimized for a class of reconfigurable logic to bring LTV based somewhat homomorphic encryption (SWHE) schemes one step closer to deployment in real-life applications. The accelerator we present is connected via a fast PCIe interface to a CPU platform to provide homomorphic evaluation services to any application that needs to support blinded computations. Specifically we introduce a number theoretical transform based multiplier architecture capable of efficiently handling very large polynomials. When synthesized for the Xilinx Virtex 7 family the presented architecture can compute the product of large polynomials in under 6.25 msec making it the fastest multiplier design of its kind currently available in the literature and is more than 102 times faster than a software implementation. Using this multiplier we can compute a relinearization operation in 526 msec. When used as an accelerator, for instance, to evaluate the AES block cipher, we estimate a per block homomorphic evaluation performance of 442 msec yielding performance gains of 28.5 and 17 times over similar CPU and GPU implementations, respectively

Throughput/Area-Efficient Accelerator of Elliptic Curve Point Multiplication over GF(2233) on FPGA

This paper presents a throughput/area-efficient hardware accelerator architecture for elliptic curve point multiplication (ECPM) computation over GF(2233). The throughput of the proposed accelerator design is optimized by reducing the total clock cycles using a bit-parallel Karatsuba modular multiplier. We employ two techniques to minimize the hardware resources: (i) a consolidated arithmetic unit where we combine a single modular adder, multiplier, and square block instead of having multiple modular operators, and (ii) an Itoh–Tsujii inversion algorithm by leveraging the existing hardware resources of the multiplier and square units for multiplicative inverse computation. An efficient finite-state-machine (FSM) controller is implemented to facilitate control functionalities. To evaluate and compare the results of the proposed accelerator architecture against state-of-the-art solutions, a figure-of-merit (FoM) metric in terms of throughput/area is defined. The implementation results after post-place-and-route simulation are reported for reconfigurable field-programmable gate array (FPGA) devices. Particular to Virtex-7 FPGA, the accelerator utilizes 3584 slices, needs 7208 clock cycles, operates on a maximum frequency of 350 MHz, computes one ECPM operation in 20.59 s, and the calculated value of FoM is 13.54. Consequently, the results and comparisons reveal that our accelerator suits applications that demand throughput and area-optimized ECPM implementations