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AI and Machine Learning on Low-Power Microcontrollers
Technical overview of TinyML on MCUs: frameworks, model optimization (quantization, pruning), accelerators, toolchains, and deployment guidance for embedded systems.
What Is Automatic Speech Recognition and GPU-Accelerated ASR
Overview of ASR (speech-to-text): pipeline, acoustic and language models, CTC training, decoding strategies, and GPU acceleration using NVIDIA NeMo and toolkits.
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.
AI Applications in Wargaming: Case Studies
Analysis of AI applications in wargaming: case studies, benefits like enhanced scenario realism and decision support, and limitations such as black-box effects and cost.
Understanding GRU in Neural Networks
Learn about Gated Recurrent Units (GRU) in neural networks, their role in sequential data processing, and applications in machine learning.
Common Deep Learning Methods for Small Object Detection
Survey of techniques for small object detection and face detection: image pyramids, FPNs, data augmentation, anchor strategies, SNIP/SNIPER training and context modeling.
Will Gemini Disrupt the Large Model Market?
Technical overview of Google Gemini, a multimodal foundation model family (Ultra, Pro, Nano), its benchmarks vs GPT-4, multimodal capabilities, and TPU efficiency.
Relationship Between AI, Machine Learning, and Deep Learning
Overview of artificial intelligence and its relationship to machine learning and deep learning, covering AI categories, ML workflow, and common deep architectures.
Using AI Tools to Improve IC Design Efficiency
Synopsys.ai and Microsoft extend Copilot into EDA with Azure OpenAI, adding GenAI features for RTL generation, formal verification assertions, and validated design workflows.
Challenges for AI Chips in the Large-Model Era
Analysis of heterogeneous computing and AI chips in the large-model era: performance gaps, CUDA ecosystem limits, pooled training, and evaluation needs.
EASY EAI Nano-TB (RV1126B) AIoT Board Data Sheet
Technical overview of the EASY EAI Nano-TB AIoT mainboard (RV1126B): quad-core Cortex-A53, up to 3 Tops NPU, dual MIPI CSI, MIPI DSI, dual GbE, WiFi 6, USB, GPIO, Linux SDK.
AI Smart Safety Helmet: Passive to Active Protection
Overview of AI smart safety helmet integrating AI vision, vital-sign and environmental sensors for real-time monitoring, alerts, positioning and intelligent management.
Understanding the Transformer Neural Network Model
Overview of the Transformer architecture: self-attention, multi-head attention, positional encoding, encoder-decoder stacks, and implications for distributed model training.
AI Infrastructure Drives Three Waves of Industrial Change
Technical overview of AI 2.0: how generative AI drives demand for large-scale compute, data pipelines, and Model-as-a-Service (MaaS) to enable industry deployments.
Deep Learning for Radar Target Detection
Survey of deep learning approaches for radar target detection, comparing two-stage and single-stage detectors (Faster R-CNN, YOLOv5), preprocessing, and deployment results.
MAX78000 Low-Power CNN AI Microcontroller
MAX78000 AI microcontroller with low-power CNN accelerator, Arm Cortex?M4 with FPU, 442 KB weight SRAM and 512 KB flash, optimized for edge inference.
Memory Challenges in AI and Machine Learning Compute
Overview of AI memory demands and new technologies: capacity, bandwidth, latency, power, reliability, and adoption challenges for future AI systems.
NVIDIA AI Workbench Simplifies AI Development
NVIDIA AI Workbench simplifies AI development with tools for RAG apps, GPU setups, and model customization across systems.
Thermal and EMI Challenges in AI Chip Design
Explore thermal and EMI challenges in AI chip design, focusing on heat dissipation and noise suppression for high-performance computing.
Survey of Deep Metric Learning
Survey of deep metric learning: formulations, sample selection and metric loss functions (contrastive, triplet, N?pair), architectures and applications in vision, audio, and text.