Deep Learning Accelerators: Optimization Strategies

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Related lectures (32)

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Data Compression and Entropy: Basics and Introduction

Introduces data compression, entropy, and the importance of reducing redundancy in data.

Efficient Storage for Analytics: Columnar Storage & Compression Techniques

Explores columnar storage, compression techniques, DSM properties, and PAX advantages for analytics.

Compression: introduction

Introduces data compression, exploring how redundancy in data can be reduced to achieve smaller file sizes without losing information.

Decomposition Storage Model: Column-Oriented Storage

Explores the advantages of Decomposition Storage Model with column-oriented storage and various compression techniques.

Image compression

Explores image compression through various approaches like pixel and block level compression, Discrete Cosine Transform, quantization, and entropy coding.

Applied Topology: Compression Schemes and Range Patches

Explores compression schemes, texture recognition, range patches, evolution, and variants on persistence in applied topology.

Model Compression Techniques: Enhancing Neural Networks

Covers model compression techniques to enhance the efficiency of large language models in production settings.

Fourier Transform: Compression and Analysis

Covers the Fourier transform, compression techniques, and spectral analysis of periodic signals.

Data Compression and Shannon's Theorem: Shannon-Fano Coding

Explores Shannon-Fano coding for efficient data compression and its comparison to Huffman coding.

Data Compression and Shannon-Fano Algorithm

Explores the Shannon-Fano algorithm for data compression and its efficiency in creating unique binary codes for letters.