Variable Latency Speculative Parallel Prefix Adders for Unsigned and Signed Operands

A variable latency adder (VLA) reduces average addition time by using speculation: the exact arithmetic function is replaced by an approximated one, that is faster and gives correct results most of the times. When speculation fails, an error detection and correction circuit gives the correct result in the following clock cycle. Previous papers investigate VLAs based on Kogge-Stone, Han-Carlson or carry select topologies, speculating that carry propagation involves only a few consecutive bits. In several applications using 2's complement representation, however, operands have a Gaussian distribution and a nontrivial portion of carry chains can be as long as the adder size. In this paper we propose five novel VLA architectures, based on Brent-Kung, Ladner-Fisher, Sklansky, Hybrid Han-Carlson, and Carry increment parallel-prefix topologies. Moreover, we present a new efficient error detection and correction technique, that makes proposed VLAs suitable for applications using 2's complement representation. In order to investigate VLAs performances, proposed architectures have been synthesized using the UMC 65 nm library, for operand lengths ranging from 32 to 128 bits. Obtained results show that proposed VLAs outperform previous speculative architectures and standard (non-speculative) adders when high-speed is required

VLSI Circuits for Approximate Computing

Approximate Computing has recently emerged as a promising solution to enhance circuits performance by relaxing the requisite on exact calculations. Multimedia and Machine Learning constitute a typical example of error resilient, albeit compute-intensive, applications. In this dissertation, the design and optimization of approximate fundamental VLSI digital blocks is investigated. In chapter one the theoretical motivations of Approximate Computing, from the VLSI perspective, are discussed. In chapter two my research activity about approximate adders is reported. In this chapter approximate adders for both traditional non-error tolerant applications and error resilient applications are discussed. In chapter three precision-scalable units are investigated. Real-time precision scalability allows adapting the precision level of the unit with the precision requirements of the applications. In this context my research activities regarding approximate Multiply-and-Accumulate and memory units are described. In chapter four a precision-scalable approximate convolver for computer vision applications is discussed. This is composed of both the approximate Multiply-and-Accumulate and memory units, presented in the chapter three