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.
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.
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.
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.
Edge AI technical overview covering distributed architecture, model lightweighting, data preprocessing, and model deployment to edge devices for real-time inference.
Technical overview of FlashAttention v1–v3: memory-aware tiling, recomputation, and FP8 GPU optimizations that reduce HBM I/O and accelerate Transformer attention.
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.
Guide to prompt design for test case generation with large models, covering element classification, role/background templates, multi?turn refinement, and Data4Test integration.
Analysis of fully connected layers' limitations on sequential data and one-hot encoding trade-offs, with embedding and RNN alternatives for efficient sequence modeling.
Analysis of an IoT smart classroom solution - hardware connectivity, data interoperability and scenario intelligence for unified, energy-efficient device management.
Summary of ten core concepts for efficient AI large models, covering model variants, parameters, context length, tokens, distillation and quantization.
Overview of AI smart safety helmet integrating AI vision, vital-sign and environmental sensors for real-time monitoring, alerts, positioning and intelligent management.
Comprehensive overview of an AI edge compute box: definition, operation, features, security, and industry use cases for real-time edge computing and computer vision.
Overview of parallel computing and acceleration for neural networks, covering data/model parallelism, GPU/TPU and software optimizations like mixed precision.
Technical analysis comparing RTX 4090 and H100 GPUs: why the 4090 is impractical for large-model training but viable for inference with optimized batching and KV cache.
Overview of convolutional neural networks: principles like padding, stride, pooling and filters, edge detection fundamentals, architecture patterns and a Keras MNIST implementation.
Technical overview of TinyML on MCUs: frameworks, model optimization (quantization, pruning), accelerators, toolchains, and deployment guidance for embedded systems.
Technical guide to installing RKLLM-Toolkit and converting/deploying the DeepSeek-R1 LLM on EASY-EAI-Orin-Nano (RK3576), covering env setup, conversion, and on-device inference.
Guide to deploying the DeepSeek LLM on Intel Arc platforms: hardware, BIOS, Ubuntu 24.10, Arc B580 drivers and OpenVINO setup to validate model inference.
Autonomous tennis-ball collection and serving robot using YOLOv5 detection and SLAM/AMCL localization, with optimized path planning and hierarchical wheel control.
Technical guide to porting DeepSeek-R1 onto RK3588-based edge hardware: model conversion, cross-compilation, board deployment, and measured CPU, memory, and NPU performance.