Section: New Results

Software Radio Programming Model

Data Flow Programming

Streaming languages have been proven to be a natural and efficient approach for taking advantage of the intrinsic parallelism of modern CPU architectures. The focus of many previous work has been to improve the throughput of streaming programs. In [27] , we rather focus on satisfying quality-of-service requirements of streaming applications executed alongside non-streaming processes. We monitor synchronous dataflow (SDF) programs at runtime both at the application and system levels, in order to identify violations of quality-of-service requirements. Our monitoring requires the programmer to provide the expected throughput of its application (e.g 25 frames per second for a video decoder), then takes full benefit from the compilation of the SDF graph to detect bottlenecks in this graph and identify causes among processor or memory overloading. It can then be used to perform dynamic adaptations of the applications in order to optimize the use of computing and memory resources.

Smart Sensors

The article [19] presents the development of a wireless wearable sensor for the continuous, long-term monitoring of cardiac activity. Heart rate assessment, as well as heart rate variability parameters are computed in real time directly on the sensor, thus only a few parameters are sent via wireless communication for power saving. Hardware and software methods for heart beat detection and variability calculation are described and preliminary tests for the evaluation of the sensor are presented. With an autonomy of 48 hours of active measurement and a Bluetooth Low Energy radio technology, this sensor will form a part of a wireless body network for the remote mobile monitoring of vital signals in clinical applications requiring automated collection of health data from multiple patients.

Cryptography

For security applications in wireless sensor networks (WSNs), choosing best algorithms in terms of energy-efficiency and of small memory requirements is a real challenge because the sensor networks are composed of low-power entities. Previous works benchmarked 12 block-ciphers on an ATMEL AVR ATtiny45 8-bit microcontroller. In [2] , most of the recent lightweight block cipher proposals, as well as some conventional block ciphers, are studied on the Texas Instruments MSP430 16-bit microcontroller . The chosen block ciphers are described with a security and an implementation summary. Implementations are then evaluated on a dedicated platform.

Hardware Arithmetic

Hardware Implementations of Fixed-Point Atan2

The atan2 function computes the polar angle arctan(x/y) of a point given by its cartesian coordinates. It is widely used in digital signal processing to recover the phase of a signal. The article [14] studies for this context the implementation of atan2 with fixed-point inputs and outputs. It compares the prevalent CORDIC shift-and-add algorithm to two multiplier-based techniques. The first one reduces the bivariate atan2 function to two functions of one variable: the reciprocal, and the arctangent. These two functions may be tabulated, or evaluated using bipartite or polynomial approximation methods. The second technique directly uses piecewise bivariate polynomial approximations, in degree 1 and degree 2. It requires larger tables but has the shortest latency. Each of these approaches requires a relevant argument reduction, which is also discussed. All the algorithms are described with the same accuracy target (faithful rounding) and implemented with similar care in FloPoCo. Based on synthesis results on FPGAs, their relevance domains are discussed.

Fixed-Point Implementations of the Reciprocal, Square Root and Reciprocal Square Root Functions

Implementations of the reciprocal, square root and reciprocal square root often share a common structure. The article [39] is a survey and comparison of methods for computing these functions. It compares classical methods (direct tabulation, multipartite tables, piecewise polynomials, Taylor-based polynomials, Newton-Raphson iterations). It also studies methods that are novel in this context: the Halley method and, more generally, the Householder method. The comparisons are made in the context of the same accuracy target (faithful rounding) and of an arbitrary fixed-point format for the inputs and outputs (precisions of up to 32 bits). Some of the methods discussed might require some form of range reduction, depending on the input range. The objective of the article is to optimize the use of fixed-size FPGA resources (block multipliers and block RAMs). The discussions and conclusions are based on synthesis results for FPGAs.

Fixed-Point Hardware Polynomials

Polynomial approximation is a general technique for the evaluation of numerical functions of one variable such as atan, reciprocal and square roots studied above. The article [38] addresses the automatic construction of fixed-point hardware polynomial evaluators. By systematically trying to balance the accuracy of all the steps that lead to an architecture, it simplifies and improves the previous body of work covering polynomial approximation, polynomial evaluation, and range reduction. This work is supported by an open-source implementation in FloPoCo.

Software Elementary Functions

Code Generators for Mathematical Functions

A typical floating-point environment includes support for a small set of about 30 mathematical functions such as exponential, logarithms and trigonometric functions. These functions are provided by mathematical software libraries (libm), typically in IEEE754 single, double and quad precision. The article [13] suggests to replace this libm paradigm by a more general approach: the on-demand generation of numerical func-tion code, on arbitrary domains and with arbitrary accuracies. First, such code generation opens up the libm function space available to programmers. It may capture a much wider set of functions, and may capture even standard functions on non-standard domains and accuracy/performance points. Second, writing libm code requires fine-tuned instruction selection and scheduling for performance, and sophisticated floating-point techniques for accuracy. Automating this task through code generation improves confidence in the code while enabling better design space exploration, and therefore better time to market, even for the libm functions. This article discusses, with examples, the new challenges of this paradigm shift, and presents the current state of open-source function code generators.

Computing Floating-Point Logarithms with Fixed-Point Operations

Elementary functions from the mathematical library input and output floating-point numbers. However it is possible to implement them purely using integer/fixed-point arithmetic. This option was not attractive between 1985 and 2005, because mainstream processor hardware supported 64-bit floating-point, but only 32-bit integers. Besides, conversions between floating-point and integer were costly. This has changed in recent years, in particular with the generalization of native 64-bit integer support. The purpose of the article [40] is therefore to reevaluate the relevance of computing floating-point functions in fixed-point. For this, several variants of the double-precision logarithm function are implemented and evaluated. Formulating the problem as a fixed-point one is easy after the range has been (classically) reduced. Then, 64-bit integers provide slightly more accuracy than 53-bit mantissa, which helps speed up the evaluation. Finally, multi-word arithmetic, critical for accurate implementations, is much faster in fixed-point, and natively supported by recent compilers. Novel techniques of argument reduction and rounding test are introduced in this context. Thanks to all this, a purely integer implementation of the correctly rounded double-precision logarithm outperforms the previous state of the art, with the worst-case execution time reduced by a factor 5. This work also introduces variants of the logarithm that input a floating-point number and output the result in fixed-point. These are shown to be both more accurate and more efficient than the traditional floating-point functions for some applications.