Date of Award

6-2019

Degree Name

MS in Electrical Engineering

Department

Electrical Engineering

Advisor

Professor Andrew Danowitz

Abstract

Sorting is often computationally intensive and can cause the application in which it is used to run slowly. To date, the quickest software sorting implementations for an N element sorting problem runs at O(nlogn). Current techniques, beyond developing better algorithms, used to accelerate sorting include the use of multiple processors or moving the sorting operation to a GPU. The use of multiple processors or a GPU can lead to increased energy consumption and heat produced by the device as compared to a single-core GPU-less implementation. To address these problems, specialized instructions and hardware units can be added to the processors to accelerate the sorting operation directly. This thesis studies and records the performance implications from implementing a sorting accelerator into a modern RISC-V processor pipeline. This thesis also explores the additional energy and area costs of implementing such hardware units in the processor.

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