TI EVM-ARC-AFE Module: Features and Applications

TI EVM-ARC-AFE evaluation module: 4-channel analog front end for AI-based DC arc detection in solar systems, supports data labeling and C2000 controlCARDs.

Precision Comparison: FP64 FP32 FP16 TF32 BF16 INT8

Concise overview of numeric precision formats, FP64, FP32, FP16, TF32, BF16 and int8, comparing bit widths, accuracy trade-offs and use cases for AI training and inference.

Analog Devices MAX78002 AI Microcontroller Datasheet

Overview of the Analog Devices MAX78002 AI microcontroller - ultra-low-power dual-core MCU with power management and edge inference use cases for battery-powered devices.

Key Steps for Edge AI: Hardware Selection and Model Deployment

Edge AI technical overview covering distributed architecture, model lightweighting, data preprocessing, and model deployment to edge devices for real-time inference.

FlashAttention 1/2/3: Transformer attention optimizations

Technical overview of FlashAttention v1–v3: memory-aware tiling, recomputation, and FP8 GPU optimizations that reduce HBM I/O and accelerate Transformer attention.

MT8391 (Genio 720) Specifications for Edge AIoT

Overview of the MediaTek MT8391 (Genio 720) edge AI platform: 6 nm octa-core CPU, 10 TOPS NPU, dual ISPs, LPDDR5 support and multi-interface connectivity for AIoT devices.

How to Get Large Models to Generate Desired Test Cases

Guide to prompt design for test case generation with large models, covering element classification, role/background templates, multi?turn refinement, and Data4Test integration.

AI Engineer Interview Questions: Recurrent Neural Networks

Analysis of fully connected layers' limitations on sequential data and one-hot encoding trade-offs, with embedding and RNN alternatives for efficient sequence modeling.

Smart Classroom IoT Solution

Analysis of an IoT smart classroom solution - hardware connectivity, data interoperability and scenario intelligence for unified, energy-efficient device management.

10 Core Concepts of Large Models

Summary of ten core concepts for efficient AI large models, covering model variants, parameters, context length, tokens, distillation and quantization.