Insights into Model Quantization in Edge AI for Enhanced Performance covering the need, Different Types and Quantization Strategies for Embedded AI

With the need for Model Pruning in Edge AI Systems for Optimal Performance, we discuss Types of Pruning, Pruning Strategies for Edge AI and Considerations

This article on Considerations for Adopting AI/ML Models in Embedded Systems covers Model Design and Optimization, Size Reduction, Latency Reduction etc.

Explore the vast landscape of embedded system processor secelction and a framework for selecting the right processor/MPU/MCU.

Article about embedded AI frameworks, exploring their need, and major frameworks like TinyML, TensorFlow Lite, PyTorch Mobile, Core ML, NVIDIA JetPack etc.

Exploring Embedded Deep Learning Algorithms such as Convolutional Neural Networks (CNNs), RNNs, Long Short-Term Memory (LSTMs) and Transformers

Article discusses how our existing cryptographic locks could be picked in a matter of seconds by quantum computers and Need for Post-Quantum Cryptography.

This article explores key ML algorithms, evaluates their suitability, discussing its mechanics, computational demands, & adaptations for constrained devices.

An article about Understanding How AI Systems Learn covering Supervised Learning, Unsupervised Learning, & Reinforcement Learning with edge AI use cases.

This article provides a high-level overview of some of the popular embedded AI compute architectures in use today covering thier features, and applications.

A Deep dive into Edge AI System Computing Elements cover the internal architecture, Advantages & limitations of CPU, GPU and AI Accelerators in AI Systems.

Second of two-part article on comparing Embedded System and Edge AI system Architectures, explores the Hardware and Software Architecture of Edge AI System.

First of two-part article on comparing Embedded System and Edge AI system Architectures, explores the Hardware and Software Architecture of Embedded System.

A journey through the Evolution of Edge AI Systems capturing Evolution of AI Algorithms and Evolution of Edge Computing, leading to convergence edge AI age.

Embark on a journey to unravel the intricacies of Edge AI, exploring its definition, advantages, necessity of Edge AI Systems, and real-world applications.

Introduction to three Deployment models of AI Systems - Cloud AI, Edge AI and Hybrid AI, comparison and some factors to choose the right Deployment Model.

This article on Application of AI Systems briefly covers use cases like Industrial Automation, Autonomous Vehicles, Healthcare, Finance and Banking etc.

This article outlines different ways for Realization of AI Systems like Search-Based AI, Logical Reasoning, Probabilistic Modeling Systems, ANNs, GPTs etc.

This article covers the Classification of Artificial Intelligence Systems including Expert Systems, Robotics, NLP, Computer Vision, Machine Learning etc.

FlexRay communication employs a highly defined FlexRay frame format and encoding that helps achieve reliable real time communication on the FlexRay network

This article covers introduces the popular FlexRay protocol covering the FlexRay basics and physical layer of the FlexRay data bus and Network Topologies

A guide to Controller Area Network covering CAN Physical Layer (PMA & PMD), CAN Data Link Layer, OSI Model mapping, Advantages and Applications of CAN Bus

A deep dive into the CAN Bus Communication covering CAN Frame Types, Bit Stuffing, Data Throughput in CAN Bus and evolution to support higher data rates