Mentor Graphics Announces New Bit-Accurate C++ Datatypes that Accelerate Algorithm Validation by 10x

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.

"By offering efficient fixed-point C datatypes, Mentor is adding a valuable link in the C-to-RTL design chain. Mentor's open standard enables Catalytic's hardware synthesis users to conduct faster algorithm validation as they convert their floating-point MATLAB descriptions to fixed-point ANSI C models, and ensure bit-width precision throughout the entire implementation flow," said Dave Burow, president and CEO of Catalytic.

Consistent, Intuitive Semantics Ensure Ease of Use

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.

"The Algorithmic C data types improve the ability of designers to move between different levels of abstraction, while minimizing inconsistencies and ambiguities when a design is transformed from algorithm to implementation," said Venkat Krishnaswamy, vice president of Applications Engineering & Services, Calypto Design Systems. "Calypto's customers are interested in bridging the gap between System and RTL for verification as well as implementation. This is a welcome technology to reduce that gap, making ESL design and verification more viable."