With the rise of Large Language Models (LLMs) and Vision Transformers (ViTs), we investigate the return from expensive "Quantization-Aware Training" (QAT), towards smarter "Post-Training Quantization" (PTQ), where the choice of "what" to quantize and "how" is guided by (sometimes) sophisticated metrics. The student is expected to perform an evaluation of such metrics and their effect to the quantization results.

Prerequisites

5G and beyond wireless communication systems gave/are expected to give rise to many new applications with stringent requirements that were not achievable with 4G or earlier systems. These requirements are: low latency, high reliability, battery life maximization among others.

To this extent, short blocklength coding has gained particular interest, and new research on efficient coding schemes is emerging. An idea previously discussed in the literature is the Ordered Statistics Decoder (OSD) [1]. Recent works in literature are addressing its strengths, its shortcomings and are suggesting ways to make this decoder more practical and efficient in the short blocklength regime.

The objective of the student is to study the seminal paper [1], that introduces the idea of ordered statistics decoding and be able to describe how the decoder works, what are its strengths, what are its limitations. Then, the student is expected to understand and explain the way of working of newer algorithms [2] [3] and explain if and how they improve performance.

[1] Soft-decision decoding of linear block codes based on ordered statistics | IEEE Journals & Magazine | IEEE Xplore

[2] Partial Ordered Statistics Decoding with Enhanced Error Patterns | IEEE Conference Publication | IEEE Xplore

[3] Box and match techniques applied to soft-decision decoding | IEEE Journals & Magazine | IEEE Xplore

Prerequisites