Enhancing an Embedded Processor Core with a Cryptographic Unit for Performance and Security

We present a set of low-cost architectural enhancements to accelerate the execution of certain arithmetic operations common in cryptographic applications on an extensible embedded processor core. The proposed enhancements are generic in the sense that they can be beneficially applied in almost any RISC processor. We implemented the enhancements in form of a cryptographic unit (CU) that offers the programmer an extended instruction set. The CU features a 128-bit wide register file and datapath, which enables it to process 128-bit words and perform 128-bit loads/stores. We analyze the speed-up factors for some arithmetic operations and public-key cryptographic algorithms obtained through these enhancements. In addition, we evaluate the hardware overhead (i.e. silicon area) of integrating the CU into an embedded RISC processor. Our experimental results show that the proposed architectural enhancements allow for a significant performance gain for both RSA and ECC at the expense of an acceptable increase in silicon area. We also demonstrate that the proposed enhancements facilitate the protection of cryptographic algorithms against certain types of side-channel attacks and present an AES implementation hardened against cache-based attacks as a case study

Efficient Neural Network Implementations on Parallel Embedded Platforms Applied to Real-Time Torque-Vectoring Optimization Using Predictions for Multi-Motor Electric Vehicles

The combination of machine learning and heterogeneous embedded platforms enables new potential for developing sophisticated control concepts which are applicable to the field of vehicle dynamics and ADAS. This interdisciplinary work provides enabler solutions -ultimately implementing fast predictions using neural networks (NNs) on field programmable gate arrays (FPGAs) and graphical processing units (GPUs)- while applying them to a challenging application: Torque Vectoring on a multi-electric-motor vehicle for enhanced vehicle dynamics. The foundation motivating this work is provided by discussing multiple domains of the technological context as well as the constraints related to the automotive field, which contrast with the attractiveness of exploiting the capabilities of new embedded platforms to apply advanced control algorithms for complex control problems. In this particular case we target enhanced vehicle dynamics on a multi-motor electric vehicle benefiting from the greater degrees of freedom and controllability offered by such powertrains. Considering the constraints of the application and the implications of the selected multivariable optimization challenge, we propose a NN to provide batch predictions for real-time optimization. This leads to the major contribution of this work: efficient NN implementations on two intrinsically parallel embedded platforms, a GPU and a FPGA, following an analysis of theoretical and practical implications of their different operating paradigms, in order to efficiently harness their computing potential while gaining insight into their peculiarities. The achieved results exceed the expectations and additionally provide a representative illustration of the strengths and weaknesses of each kind of platform. Consequently, having shown the applicability of the proposed solutions, this work contributes valuable enablers also for further developments following similar fundamental principles.Some of the results presented in this work are related to activities within the 3Ccar project, which has received funding from ECSEL Joint Undertaking under grant agreement No. 662192. This Joint Undertaking received support from the European Union’s Horizon 2020 research and innovation programme and Germany, Austria, Czech Republic, Romania, Belgium, United Kingdom, France, Netherlands, Latvia, Finland, Spain, Italy, Lithuania. This work was also partly supported by the project ENABLES3, which received funding from ECSEL Joint Undertaking under grant agreement No. 692455-2

Empowering parallel computing with field programmable gate arrays

After more than 30 years, reconfigurable computing has grown from a concept to a mature field of science and technology. The cornerstone of this evolution is the field programmable gate array, a building block enabling the configuration of a custom hardware architecture. The departure from static von Neumannlike architectures opens the way to eliminate the instruction overhead and to optimize the execution speed and power consumption. FPGAs now live in a growing ecosystem of development tools, enabling software programmers to map algorithms directly onto hardware. Applications abound in many directions, including data centers, IoT, AI, image processing and space exploration. The increasing success of FPGAs is largely due to an improved toolchain with solid high-level synthesis support as well as a better integration with processor and memory systems. On the other hand, long compile times and complex design exploration remain areas for improvement. In this paper we address the evolution of FPGAs towards advanced multi-functional accelerators, discuss different programming models and their HLS language implementations, as well as high-performance tuning of FPGAs integrated into a heterogeneous platform. We pinpoint fallacies and pitfalls, and identify opportunities for language enhancements and architectural refinements

GOES-R Algorithms: A Common Science and Engineering Design and Development Approach for Delivering Next Generation Environmental Data Products

GOES-R, the next generation of the National Oceanic and Atmospheric Administration’s (NOAA) Geostationary Operational Environmental Satellite (GOES) System, represents a new technological era in operational geostationary environmental satellite systems. GOES-R will provide advanced products that describe the state of the atmosphere, land, oceans, and solar/ space environments over the western hemisphere. The Harris GOES-R Ground Segment team will provide the software, based on government-supplied algorithms, and engineering infrastructures designed to produce and distribute these next-generation data products. The Harris GOES-R Team has adopted an integrated applied science and engineering approach that combines rigorous system engineering methods, with modern software design elements to facilitate the transition of algorithms for Level 1 and 2+ products to operational software. The Harris Team GOES-R GS algorithm framework, which includes a common data model interface, provides general design principles and standardized methods for developing general algorithm services, interfacing to external data, generating intermediate and L1b and L2 products and implementing common algorithm features such as metadata generation and error handling. This work presents the suite of GOES-R products, their properties and the process by which the related requirements are maintained during the complete design/development life-cycle. It also describes the algorithm architecture/engineering approach that will be used to deploy these algorithms, and provides a preliminary implementation road map for the development of the GOES-R GS software infrastructure, and a view into the integration of the framework and data model into the final design