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.