Team Arénaire

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Section: Scientific Foundations

Hardware Arithmetic

A given computing application may be implemented using different technologies, with a large range of trade-offs between the various aspects of performance, unit cost, and non-recurring costs (including development effort).

In each case, the computation is broken down into elementary operations, executed by elementary hardware elements, or arithmetic operators . In the software approach, the operators used are those provided by the microprocessor. In the ASIC or FPGA approaches, these operators have to be built by the designer, or taken from libraries. Our goals include studying operators for inclusion in microprocessors and developing hardware libraries for ASICs or FPGAs.


Operators under study. Research is active on algorithms for the following operations:

A hardware implementation may lead to better performance than a software implementation for two main reasons: parallelism and specialization. The second factor, from the arithmetic point of view, means that specific data types and specific operators, which would require costly emulation on a processor, may be used. For example, some cryptography applications are based on modular arithmetic and bit permutations, for which efficient specific operators can be designed. Other examples include standard representations with non-standard sizes, and specific operations such as multiplication by constants.


Hardware-oriented algorithms. Many algorithms are available for the implementation of elementary operators (see for instance  [55] ). For example, there are two classes of division algorithms: digit-recurrence and function iteration. The choice of an algorithm for the implementation of an operation depends on, and sometimes imposes, the choice of a number representation. Besides, there are usually technological constraints such as the area and power budget, and the available low-level libraries.

The choice of the number systems used for the intermediate results is crucial. For example, a redundant system, in which a number may have several encodings, will allow for more design freedom and more parallelism, hence faster designs. However, the hardware cost can be higher. As another example, the power consumption of a circuit depends, among other parameters, on its activity, which in turn depends on the distribution of the values of the inputs, hence again on the number system.

Alternatives exist at many levels in this algorithm exploration. For instance, an intermediate result may be either computed, or recovered from a precomputed table.


Parameter exploration. Once an algorithm is chosen, optimizing its implementation for area, delay, accuracy, or energy consumption is the next challenge. The best solution depends on the requirements of the application and on the target technology. Parameters which may vary include the radix of the number representations, the granularity of the iterations (between many simple iterations, or fewer coarser ones), the internal accuracies used, the size of the tables (see  [52] for an illustration), etc.

The parameter space quickly becomes huge, and the expertise of the designer has to be automated. Indeed, we do not design operators, but operator generators , programs that take a specification and some constraints as input, and output a synthesizable description of an operator.


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