Tech

Comparing Efficiency Strategies for LLM Deployment and Summarizing PowerInfer‑2’s Impact

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Table of Links Abstract and 1. Introduction Background and Motivation PowerInfer-2 Overview Neuron-Aware Runtime Inference Execution Plan Generation Implementation Evaluation Related Work Conclusion and References 8 Related Work Resource-Efficient LLM. Deploying LLMs on resourcer-estricted devices has become more and more popular [37]. A representative framework is MLC-LLM [33], which enables native deployment of many large … Read more

Apple brings its App Store to the web

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Apple has launched its App Store on the web, offering a central hub where you can browse through different categories of apps across all of the company’s devices, as spotted earlier by MacRumors and 9to5Mac. Now, when you navigate to apps.apple.com, you’ll see the revamped interface instead of a webpage that just contains information about … Read more

Performance Evaluation of PowerInfer‑2: Offloading, Prefill, and In‑Memory Efficiency

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Table of Links Abstract and 1. Introduction Background and Motivation PowerInfer-2 Overview Neuron-Aware Runtime Inference Execution Plan Generation Implementation Evaluation Related Work Conclusion and References 7 Evaluation In this section, we evaluate the performance of PowerInfer-2 for various models and smartphone hardwares. 7.1 Experimental Setup Hardware. We select one high-end and one mid-end OnePlus [25] … Read more

How PowerInfer‑2 Turns Your Smartphone Into an AI Workstation

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Table of Links Abstract and 1. Introduction Background and Motivation PowerInfer-2 Overview Neuron-Aware Runtime Inference Execution Plan Generation Implementation Evaluation Related Work Conclusion and References 5 Execution Plan Generation Today’s smartphones are equipped with a variety of hardware specifications, such as differing CPU capabilities, I/O throughput, and DRAM sizes. Users deploying LLMs on these devices … Read more

Apple TV’s new name now comes with a new sound

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Apple has shared a new intro sound and video that will accompany things you watch on the newly-renamed Apple TV streaming service. Apple rebranded Apple TV Plus to just Apple TV last month, and the previous intro prominently highlighted the plus, so it makes sense that Apple made a new version to accompany the updated … Read more

Why Log Semantics Matter More Than Sequence Data in Detecting Anomalies

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Table of links Abstract 1 Introduction 2 Background and Related Work 2.1 Different Formulations of the Log-based Anomaly Detection Task 2.2 Supervised v.s. Unsupervised 2.3 Information within Log Data 2.4 Fix-Window Grouping 2.5 Related Works 3 A Configurable Transformer-based Anomaly Detection Approach 3.1 Problem Formulation 3.2 Log Parsing and Log Embedding 3.3 Positional & Temporal … Read more

Transformer Models Outperform Traditional Algorithms in Log Anomaly Detection

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Table of links Abstract 1 Introduction 2 Background and Related Work 2.1 Different Formulations of the Log-based Anomaly Detection Task 2.2 Supervised v.s. Unsupervised 2.3 Information within Log Data 2.4 Fix-Window Grouping 2.5 Related Works 3 A Configurable Transformer-based Anomaly Detection Approach 3.1 Problem Formulation 3.2 Log Parsing and Log Embedding 3.3 Positional & Temporal … Read more

How Transformer Models Detect Anomalies in System Logs

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Table of links Abstract 1 Introduction 2 Background and Related Work 2.1 Different Formulations of the Log-based Anomaly Detection Task 2.2 Supervised v.s. Unsupervised 2.3 Information within Log Data 2.4 Fix-Window Grouping 2.5 Related Works 3 A Configurable Transformer-based Anomaly Detection Approach 3.1 Problem Formulation 3.2 Log Parsing and Log Embedding 3.3 Positional & Temporal … Read more

Transformer-Based Anomaly Detection Using Log Sequence Embeddings

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Table of links Abstract 1 Introduction 2 Background and Related Work 2.1 Different Formulations of the Log-based Anomaly Detection Task 2.2 Supervised v.s. Unsupervised 2.3 Information within Log Data 2.4 Fix-Window Grouping 2.5 Related Works 3 A Configurable Transformer-based Anomaly Detection Approach 3.1 Problem Formulation 3.2 Log Parsing and Log Embedding 3.3 Positional & Temporal … Read more

An Overview of Log-Based Anomaly Detection Techniques

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Table of links Abstract 1 Introduction 2 Background and Related Work 2.1 Different Formulations of the Log-based Anomaly Detection Task 2.2 Supervised v.s. Unsupervised 2.3 Information within Log Data 2.4 Fix-Window Grouping 2.5 Related Works 3 A Configurable Transformer-based Anomaly Detection Approach 3.1 Problem Formulation 3.2 Log Parsing and Log Embedding 3.3 Positional & Temporal … Read more

A Transformer Approach to Log-Based Anomaly Detection

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:::info Authors: Xingfang Wu Heng Li Foutse Khomh ::: Table of links Abstract 1 Introduction 2 Background and Related Work 2.1 Different Formulations of the Log-based Anomaly Detection Task 2.2 Supervised v.s. Unsupervised 2.3 Information within Log Data 2.4 Fix-Window Grouping 2.5 Related Works 3 A Configurable Transformer-based Anomaly Detection Approach 3.1 Problem Formulation 3.2 … Read more