High Level Synthesis and Evaluation of the Secure Hash Standard for FPGAs

Secure hash algorithms (SHAs) are important components of cryptographic applications. SHA performance on central processing units (CPUs) is slow, therefore, acceleration must be done using hardware such as Field Programmable Gate Arrays (FPGAs). Considerable work has been done in academia using FPGAs to accelerate SHAs. These designs were implemented using Hardware Description Language (HDL) based design methodologies, which are tedious and time consuming. High Level Synthesis (HLS) enables designers to synthesize optimized FPGA hardware from algorithm specifications in programming languages such as C/C++. This substantially reduces the design cost and time. In this thesis, the Altera SDK for OpenCL (AOCL) HLS tool was used to synthesize the SHAs on FPGAs and to explore the design space of the algorithms. The results were evaluated against the previous HDL based designs. Synthesized FPGA hardware performance was comparable to the HDL based designs despite the simpler and faster design process

Autotuning the Intel HLS Compiler using the Opentuner Framework

High level synthesis (HLS) tools can be used to improve design flow and decrease verification times for field programmable gate array (FPGA) and application specific integrated circuit (ASIC) design. The Intel HLS Compiler is a high level synthesis tool that takes in untimed C/C++ as input and generates production-quality register transfer level (RTL) code that is optimized for Intel FPGAs. The translation does, however, require multiple iterations and manual optimizations to get comparable synthesized results to that of a solution written in a hardware descriptive language. The synthesis results can vary greatly based upon coding style and optimization techniques, and typically require an in-depth knowledge of FPGAs to fully optimize the translation which limits the audience of the tool. The extra abstraction that the C/C++ source code presents can also make it difficult to meet more specific design requirements; this includes designs to meet specific resource usage or performance based metrics. To improve the quality of results generated by the Intel HLS Compiler without a manual iterative process that requires an in-depth knowledge of FPGAs, this research proposes a method of automating some of the optimization techniques that improve the synthesized design through an autotuning process. The proposed approach utilizes the PyCParser library to parse C source files and the OpenTuner Framework to autotune the synthesis to provide a method that generates results that better meet the needs of the designer's requirements through lower FPGA resource usage or increased design performance. Such functionality is not currently available in Intel's commercial tools. The proposed approach was tested with the CHStone Benchmarking Suite of C programs as well as a standard digital signal processing finite impulse response filter. The results show that the commercial HLS tool can be automatically autotuned through placeholder injection using a source parsing tool for C code and using the OpenTuner Framework to autotune the results. For designs that are small in nature and include conducive structures to be autotuned, the results indicate resource usage reductions and/or performance increases of up to 40% as compared to the default Intel HLS Compiler results. The method developed in this research also allows additional design targets to be specified through the autotuner for consideration in the synthesized design which can yield results that are better matched to a design's requirements

Study of OpenCL processing models for FPGA device

In our study, we present the results of the implementation of SHA-512 algorithm in FPGA. The distinguished element of our work is that we conducted the work using OpenCL for FPGA which is a relatively new development method for reconfigurable logic. We examine the loop unrolling; as the OpenCL performance optimisation method, and compare the efficiency of the different kernel implementation types: NDRange, Single-Work Item, and SIMD kernels. In conclusions, we compare metrics of the created FPGA accelerator to the corresponding GPGPU solutions. Also, our paper is accompanied by the source code repository to allow the reader to follow and extend our survey

RSA in hardware

textThis report presents the RSA encryption and decryption schemes and discusses several methods for expediting the computations required, specifically the modular exponentiation operation that is required for RSA. A hardware implementation of the CIOS (Coarsely Integrated Operand Scanning) algorithm for modular multiplication is attempted on a XILINX Spartan3 FPGA in the TLL-5000 development platform used at the University of Texas at Austin. The development of the hardware is discussed in detail and some Verilog source code is provided for an implementation of modular multiplication. Some source code is also provided for an RSA executable to run on the TLL-6219 ARM-based development platform, to be used to generate test vectors.Electrical and Computer Engineerin

Towards hardware as a reconfigurable, elastic, and specialized service

As modern Data Center workloads become increasingly complex, constrained, and critical, mainstream CPU-centric computing has had ever more difficulty in keeping pace. Future data centers are moving towards a more fluid and heterogeneous model, with computation and communication no longer localized to commodity CPUs and routers. Next generation data-centric Data Centers will compute everywhere, whether data is stationary (e.g. in memory) or on the move (e.g. in network). While deploying FPGAs in NICS, as co-processors, in the router, and in Bump-in-the-Wire configurations is a step towards implementing the data-centric model, it is only part of the overall solution. The other part is actually leveraging this reconfigurable hardware. For this to happen, two problems must be addressed: code generation and deployment generation. By code generation we mean transforming abstract representations of an algorithm into equivalent hardware. Deployment generation refers to the runtime support needed to facilitate the execution of this hardware on an FPGA. Efforts at creating supporting tools in these two areas have thus far provided limited benefits. This is because the efforts are limited in one or more of the following ways: They i) do not provide fundamental solutions to a number of challenges, which makes them useful only to a limited group of (mostly) hardware developers, ii) are constrained in their scope, or iii) are ad hoc, i.e., specific to a single usage context, FPGA vendor, or Data Center configuration. Moreover, efforts in these areas have largely been mutually exclusive, which results in incompatibility across development layers; this requires wrappers to be designed to make interfaces compatible. As a result there is significant complexity and effort required to code and deploy efficient custom hardware for FPGAs; effort that may be orders-of-magnitude greater than for analogous software environments. The goal of this dissertation is to create a framework that enables reconfigurable logic in Data Centers to be targeted with the same level of effort as for a single CPU core. The underlying mechanism to this is a framework, which we refer to as Hardware as a Reconfigurable, Elastic and Specialized Service, or HaaRNESS. In this dissertation, we address two of the core challenges of HaaRNESS: reducing the complexity of code generation by constraining High Level Synthesis (HLS) toolflows, and replacing ad hoc models of deployment generation by generalizing and formalizing what is needed for a hardware Operating System. These parts are unified by the back-end of HLS toolflows which link generated compute pipelines with the operating system, and provide appropriate APIs, wrappers, and software runtimes. The contributions of this dissertation are the following: i) an empirically guided set of systematic transformations for generating high quality HLS code; ii) a framework for instrumenting HLS compiler to identify and remove optimization blockers; iii) a framework for RTL simulation and IP generation of HLS kernels for rapid turnaround; and iv) a framework for generalization and formalization of hardware operating systems to address the {\it ad hoc}'ness of existing deployment generation and ensure uniform structure and APIs

Private and Public-Key Side-Channel Threats Against Hardware Accelerated Cryptosystems

Modern side-channel attacks (SCA) have the ability to reveal sensitive data from non-protected hardware implementations of cryptographic accelerators whether they be private or public-key systems. These protocols include but are not limited to symmetric, private-key encryption using AES-128, 192, 256, or public-key cryptosystems using elliptic curve cryptography (ECC). Traditionally, scalar point (SP) operations are compelled to be high-speed at any cost to reduce point multiplication latency. The majority of high-speed architectures of contemporary elliptic curve protocols rely on non-secure SP algorithms. This thesis delivers a novel design, analysis, and successful results from a custom differential power analysis attack on AES-128. The resulting SCA can break any 16-byte master key the sophisticated cipher uses and it\u27s direct applications towards public-key cryptosystems will become clear. Further, the architecture of a SCA resistant scalar point algorithm accompanied by an implementation of an optimized serial multiplier will be constructed. The optimized hardware design of the multiplier is highly modular and can use either NIST approved 233 & 283-bit Kobliz curves utilizing a polynomial basis. The proposed architecture will be implemented on Kintex-7 FPGA to later be integrated with the ARM Cortex-A9 processor on the Zynq-7000 AP SoC (XC7Z045) for seamless data transfer and analysis of the vulnerabilities SCAs can exploit

Hybrid PUF Design using Bistable Ring PUF and Chaotic Network

Physical Unclonable Function(PUF) is lightweight hardware that provides affordable security for electronic devices and systems which can eliminate the use of the conventional cryptographic system which uses large area and storage. Among the several models, Bi-stable Ring PUF(BR-PUF) is considered as a secure and efficient PUF model since it has no mathematical model still found. In this thesis, we proposed a modified design called a hybrid model of BR-PUF and a Chaotic network to improve the BR-PUF resilience against machine learning attacks. We experimented with the current modification XOR technique to analyze the uniqueness, reliability and resource consumption. The proposed PUF was implemented on Xilinx Artix 7 FPGA and the PUF metrics were captured and compared with the results of XOR-ed based PUF integration techniques. The lightweight PUF model was achieved with 16% resource reduction when compared to XOR-ed BR PUF with no compromise in PUF quality