Mentor Graphics Announces Algorithmic C Datatypes
Mentor Graphics Corporation has announced the availability of Algorithmic C datatypes, new high-speed datatypes based on ANSI C++. These arbitrary-bit-width datatypes enable algorithm, system and hardware designers to precisely model bit-true behavior in C++ specifications while accelerating simulation speeds by 10-200x.
Algorithm and system designers need to specify bit widths to create bit-accurate models used for system and algorithm validation. Previous bit-width specifications require one of two compromises: designers must either use native processor precision (64-bits for integer), requiring them to truncate the results and thus introduce artificial corner cases; or they must create a model using an arbitrary bit width resulting in much slower simulation speeds.
Based on universal standard ANSI C++, the new integer and fixed-point Algorithmic C datatypes offer the best of both worlds, allowing algorithm and system designers to specify arbitrary bit widths while improving simulation performance by 10-200x versus other datatypes such as SystemC. Arbitrary bit widths are also critical for high-level synthesis, as they allow hardware designers to make tradeoffs between hardware size and numerical precision. For example, a hardware designer would use arbitrary bit widths to explore tradeoffs in area, performance, or power versus image quality in a video application.
The new Algorithmic C datatypes also solve problems of semantic consistency inherent in previous datatypes. The semantics of Algorithmic C datatypes are intuitive for users familiar with ANSI C++, enabling designers to become proficient in just one day. In contrast, there is a semantic divide in SystemC between the limited precision datatypes (sc_int or sc_fixed_fast), and the arbitrary precision datatypes (sc_bigint or sc_fixed). Proficiency in one datatype does not ensure familiarity with the others, making it error prone to adjust bit-width in a given design. Consequently, one must invest significant effort to master the coding style of both datatypes in order to employ them in an optimal design implementation.
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