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Register a new userAn Open-source Library of Large Integer Polynomial Multipliers
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Added by
Zain Ul Abideen
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Polynomial multiplication is a bottleneck in most of the public-key cryptography protocols, including Elliptic-curve cryptography and several of the post-quantum cryptography algorithms presently being studied. In this paper, we present a library of various large integer polynomial multipliers to be used in hardware cryptocores. Our library contains both digitized and non-digitized multiplier flavours for circuit designers to choose from. The library is supported by a C++ generator that automatically produces the multipliers logic in Verilog HDL that is amenable for FPGA and ASIC designs. Moreover, for ASICs, it also generates configurable and parameterizable synthesis scripts. The features of the generator allow for a quick generation and assessment of several architectures at the same time, thus allowing a designer to easily explore the (complex) optimization search space of polynomial multiplication.
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PCMSolver is an open-source library for continuum electrostatic solvation. It can be combined with any quantum chemistry code and requires a minimal interface with the host program, greatly reducing programming effort. As input, PCMSolver needs only the molecular geometry to generate the cavity and the expectation value of the molecular electrostatic potential on the cavity surface. It then returns the solvent polarization back to the host program. The design is powerful and versatile: minimal loss of performance is expected, and a standard single point self-consistent field implementation requires no more than 2 days of work. We provide a brief theoretical overview, followed by two tutorials: one aimed at quantum chemistry program developers wanting to interface their code with PCMSolver, the other aimed at contributors to the library. We finally illustrate past and ongoing work, showing the librarys features, combined with several quantum chemistry programs.
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient devices and systems are extremely valuable across a broad range of application domains. In scientific domains, real-time near-sensor processing can drastically improve experimental design and accelerate scientific discoveries. To support domain scientists, we have developed hls4ml, an open-source software-hardware codesign workflow to interpret and translate machine learning algorithms for implementation with both FPGA and ASIC technologies. We expand on previous hls4ml work by extending capabilities and techniques towards low-power implementations and increased usability: new Python APIs, quantization-aware pruning, end-to-end FPGA workflows, long pipeline kernels for low power, and new device backends include an ASIC workflow. Taken together, these and continued efforts in hls4ml will arm a new generation of domain scientists with accessible, efficient, and powerful tools for machine-learning-accelerated discovery.
The broad application of artificial intelligence techniques ranging from self-driving vehicles to advanced medical diagnostics afford many benefits. Federated learning is a new breed of artificial intelligence, offering techniques to help bridge the gap between personal data protection and utilization for research and commercial deployment, especially in the use-cases where security and privacy are the key concerns. Here, we present OpenFed, an open-source software framework to simultaneously address the demands for data protection and utilization. In practice, OpenFed enables state-of-the-art model development in low-trust environments despite limited local data availability, which lays the groundwork for sustainable collaborative model development and commercial deployment by alleviating concerns of asset protection. In addition, OpenFed also provides an end-to-end toolkit to facilitate federated learning algorithm development as well as several benchmarks to fair performance comparison under diverse computing paradigms and configurations.
We propose a new algorithm for multiplying dense polynomials with integer coefficients in a parallel fashion, targeting multi-core processor architectures. Complexity estimates and experimental comparisons demonstrate the advantages of this new approach.
We introduce the Control Toolbox (CT), an open-source C++ library for efficient modeling, control, estimation, trajectory optimization and Model Predictive Control. The CT is applicable to a broad class of dynamic systems but features interfaces to modeling tools specifically designed for robotic applications. This paper outlines the general concept of the toolbox, its main building blocks, and highlights selected application examples. The library contains several tools to design and evaluate controllers, model dynamical systems and solve optimal control problems. The CT was designed for intuitive modeling of systems governed by ordinary differential or difference equations. It supports rapid prototyping of cost functions and constraints and provides standard interfaces for different optimal control solvers. To date, we support Single Shooting, the iterative Linear-Quadratic Regulator, Gauss-Newton Multiple Shooting and classical Direct Multiple Shooting. We provide interfaces to general purpose NLP solvers and Riccati-based linear-quadratic optimal control solvers. The CT was designed to solve large-scale optimal control and estimation problems efficiently and allows for online control of dynamic systems. Some of the key features to enable fast run-time performance are full compatibility with Automatic Differentiation, derivative code generation, and multi-threading. Still, the CT is designed as a modular framework whose building blocks can also be used for other control and estimation applications such as inverse dynamics control, extended Kalman filters or kinematic planning.
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