Please complete the paper according to the following instructions.



Writing Assignment

3: Related Works Section



Objective:

To

extend the previous assignment by adding a comprehensive related works section.

This section will require students to conduct a thorough literature review and

present their findings in a structured way.



Requirements:

1.      Length: Minimum of five pages (excluding references).

2.      Content:

 - Literature Search: Conduct a

comprehensive literature search using academic databases, online journals, and

relevant publications. Focus on peer-reviewed articles, conference papers,

books, and other credible sources.

 - Review Structure: For each

reviewed study, include:

    * Key findings

    * Theories and methodologies used

    * Gaps and limitations in the

existing research

 - Common Themes and Trends:

Identify and discuss common themes or trends that emerge from the literature.

 - Research Gaps: Again, highlight

research gaps and limitations for each reviewed study.

Formatting:

Use the Elsevier Article (elsarticle) LaTeX template.



Ensure the document follows the formatting and style guidelines provided in the

template.

Submission:

-         Submit both the PDF and the LaTeX source files (Overleaf project

zip).

-         The submitted assignment should include the content of the previous

assignment and this assignment. Please note that you can update the previous

content that you are going to include in this assignment to make the current

assignment more organized. 



Instructions:

1. Literature Search:

   - Use academic databases

(e.g., IEEE Xplore, ACM Digital Library, Google Scholar).

   - Look for peer-reviewed articles,

conference papers, books, and other credible sources relevant to your research

area.

   - Take detailed notes on key findings,

theories, methodologies, and gaps in each study.

2. Review Structure:

   - Organize the literature

review into coherent paragraphs.

   - Each part should cover a specific

aspect/way to solve the problem related to the research topic.

   - For each reviewed work, include:

      * Citation: Properly cite the

source.

      * Summary: Provide a brief summary

of the key findings.

      * Theories and Methodologies:

Discuss the theories and methodologies used in the study.

      * Gaps and Limitations: Identify

and discuss any gaps or limitations in the research.

3. Common Themes and Trends:

   - Analyze the reviewed

literature to identify common solution methods or trends.

   - Discuss these methods and how they

relate to your research topic.

4. Research Gaps:

   - Highlight the research

gaps identified in the reviewed studies.

   - Discuss how your research aims to

address these gaps.

Evaluation

Criteria:

-         Content Quality: Depth and clarity of the literature review.

-         Review Structure: Organization and coherence of the literature

review.

-         Analysis: Effectiveness in identifying common themes and research

gaps.

-         Formatting and Presentation: Adherence to the Elsevier Article

(elsarticle) template guidelines.

-         References: Proper citation and listing of all referenced works.



Tips:

-         Use/update the table from the previous assignment to summarize key

aspects of the reviewed literature.

-         Ensure your document is well-organized and free of grammatical and

typographical errors.

-         Review the Elsevier Article (elsarticle) template guidelines

thoroughly to ensure compliance.







\documentclass[preprint,12pt,nopreprintline]{elsarticle}



\usepackage{amssymb}



\begin{document}



\begin{frontmatter}



\title{Evaluating Post-Training Quantization of Weights, Activations, and KV Caches for Edge Applications}



\author[inst1]{Si-hoon Song}



\affiliation[inst1]{organization={Department of Artificial Intelligence},

            addressline={Sungkyunkwan University, }, 

            city={Suwon, Gyeonggi-do},

            postcode={16419}, 

            state={Gyeonggi-do},

            country={Republic of Korea}}



\author[inst2]{Jee-Hyong Lee}



\affiliation[inst2]{organization={Department of Artificial Intelligence},

            addressline={Sungkyunkwan University, }, 

            city={Suwon, Gyeonggi-do},

            postcode={16419}, 

            state={Gyeonggi-do},

            country={Republic of Korea}}



\begin{abstract}

This paper introduces a simulator-based approach for evaluating the feasibility of various post-training quantization techniques for edge computing applications. By incorporating quantization of weights, activations, and KV caches within the simulation, the framework allows developers to explore the impact of different quantization strategies on model performance and memory bandwidth even before the final hardware configuration is determined. This flexibility aids in selecting optimal quantization schemes and enables informed hardware-software co-design choices. Results demonstrate the effectiveness of the simulator in quantifying the performance trade-offs of diverse quantization methods, facilitating the development of efficient edge computing models. The simulator's potential to guide model-level modifications and future integration of on-the-fly quantization modules into hardware components is also highlighted.

\end{abstract}



\end{frontmatter}



\section{Introduction}

\label{sec:sample1}

The proliferation of deep learning models across various applications has fueled a growing demand for efficient edge computing solutions. Edge devices, characterized by their resource constraints, necessitate models that are not only accurate but also compact and computationally efficient. Post-training quantization (PTQ) has emerged as a promising technique to address this need. By reducing the precision of model weights and activations, PTQ enables significant reductions in model size and memory footprint, leading to faster inference and reduced energy consumption.



However, implementing PTQ for edge applications presents several challenges. The diverse hardware architectures and limited computational capabilities of edge devices demand careful consideration of quantization strategies. Additionally, the impact of quantization on model performance and accuracy needs to be thoroughly evaluated before deployment.



This paper proposes a simulator-based framework to evaluate various PTQ techniques for edge computing scenarios. The framework incorporates quantization of weights, activations, and key-value (KV) caches, providing a comprehensive assessment of different quantization strategies. By simulating the quantization process, developers can gain insights into the trade-offs between model performance, memory bandwidth, and hardware requirements, even before the final hardware configuration is finalized.



The primary contributions of this work are:



1. \textbf{Simulator-Based Evaluation}: We introduce a simulator that enables the evaluation of diverse PTQ techniques for edge applications. The simulator considers quantization of weights, activations, and KV caches, providing a holistic view of the quantization process.



2. \textbf{Flexibility and Hardware-Software Co-design}: The framework's flexibility allows developers to experiment with different quantization strategies and explore their impact on model performance and memory bandwidth. This flexibility aids in making informed hardware-software co-design choices.



3. \textbf{Performance Quantification}: We demonstrate the effectiveness of the simulator in quantifying the performance trade-offs of various quantization methods. This information helps developers select optimal quantization schemes for their specific edge computing applications.



4. \textbf{Model-Level Modifications and Future Integration}: The simulator's potential to guide model-level modifications and future integration of on-the-fly quantization modules into hardware components is also highlighted.



The rest of the paper is organized as follows: Section 2 provides an overview of related work in the field of PTQ for edge computing. Section 3 details the proposed simulator-based framework, including the quantization techniques and simulation methodology. Section 4 presents the experimental results, demonstrating the effectiveness of the simulator in evaluating PTQ strategies. Finally, Section 5 concludes the paper and outlines future research directions.



\section{Related Work}

The pursuit of efficient large language model (LLM) deployment, especially on resource-constrained edge devices, has spurred extensive research into post-training quantization (PTQ). Early efforts primarily focused on uniform quantization of weights and activations, with notable advancements in techniques like GPTQ \cite{gptq} and LLM.int8() \cite{ACEQS}.  These methods demonstrated significant compression ratios with minimal accuracy degradation. 



However, as LLMs grew in size and complexity, the limitations of uniform quantization became evident. The diverse distribution of weights and activations across different layers necessitated more nuanced approaches. This led to the exploration of mixed-precision quantization, where different layers or even individual weights are quantized to varying bit-widths. Techniques like APTQ \cite{APTQ} and No Token Left Behind \cite{NoToken} exemplify this trend, showcasing improved performance-compression trade-offs.



In parallel, the role of the key-value (KV) cache in LLM inference has gained prominence. As context windows expanded, the KV cache became a major memory bottleneck. Consequently, research into KV cache quantization emerged, with methods like KVQuant \cite{KVQuant} and KIVI \cite{KIVI} demonstrating promising results in reducing memory footprint without significantly impacting accuracy. 



More recently, the quest for even higher compression ratios has driven investigations into extreme low-bit quantization, including binary and ternary quantization.  Techniques like BiLLM \cite{BiLLM} and OneBit \cite{OneBit} push the boundaries of quantization, albeit with the trade-off of increased complexity and potential accuracy loss.



The evolution of PTQ techniques for LLMs is a testament to the dynamic nature of this research field. While substantial progress has been made, challenges remain, particularly in balancing compression ratios, accuracy preservation, and computational efficiency. The simulator-based framework proposed in this paper aims to contribute to this ongoing endeavor by providing a comprehensive platform for evaluating diverse PTQ strategies, ultimately facilitating the deployment of efficient and accurate LLMs on edge devices. 



\section{Simulator-Based Framework}

\label{sec:sample2}



This section provides a detailed description of the proposed simulator-based framework for evaluating post-training quantization techniques. We begin by outlining the specific quantization methods considered, followed by a comprehensive explanation of the simulation methodology.



\subsection{Quantization Techniques}



Our framework encompasses a range of quantization techniques to provide a comprehensive evaluation platform:



\textbf{Weight Quantization}: We implement both uniform and non-uniform quantization schemes for weights. Uniform quantization assigns a fixed number of bits to all weights, while non-uniform quantization allows for varying bit-widths across different layers or even individual weights. This flexibility enables us to explore the trade-offs between compression and accuracy preservation.



\textbf{Activation Quantization}: Similar to weight quantization, we support both uniform and non-uniform quantization for activations. However, the dynamic range of activations can vary significantly during inference, posing additional challenges. We employ techniques like outlier clipping and adaptive scaling to mitigate quantization errors and maintain model performance.



\textbf{KV Cache Quantization}: The key-value (KV) cache, crucial for storing contextual information in LLMs, can consume a substantial portion of memory. Our framework incorporates quantization of the KV cache using methods like product quantization and vector quantization. These techniques aim to reduce the memory footprint of the KV cache without compromising its effectiveness.



\subsection{Simulation Methodology}

The core of our framework is a simulator that emulates the quantization process and its impact on model performance and memory bandwidth. The simulation proceeds as follows:



1. \textbf{Model Selection}: We begin by selecting a pre-trained LLM and its corresponding dataset.



2. \textbf{Quantization Configuration}: We specify the desired quantization techniques and their parameters, such as bit-widths, scaling factors, and quantization algorithms.



3. \textbf{Quantization Simulation}: The simulator quantizes the model weights, activations, and KV cache according to the specified configuration. It tracks the resulting changes in model size, memory footprint, and computational complexity.



4. \textbf{Performance Evaluation}: The quantized model is evaluated on the chosen dataset, and key metrics such as accuracy, perplexity, and inference speed are recorded.



5. \textbf{Memory Bandwidth Analysis}: The simulator estimates the memory bandwidth requirements of the quantized model, considering factors like data transfers between different memory hierarchies and the impact of quantization on data access patterns.



6. \textbf{Hardware-Software Co-design Exploration}: The framework allows developers to experiment with different hardware configurations and explore their interplay with the quantized model. This enables informed decisions regarding hardware-software co-design choices.



By repeating steps 2-6 with various quantization configurations and hardware setups, developers can gain valuable insights into the performance trade-offs of different PTQ strategies. This information aids in selecting optimal quantization schemes and making informed design decisions for efficient edge computing models. 



In the next section, we present experimental results demonstrating the effectiveness of our simulator-based framework in evaluating PTQ techniques for edge applications. 





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