ALLPCB
OpenAI Sora: a new AI model for video generation
Technical overview of OpenAI's Sora and its video generation capabilities, core machine learning foundations, and potential impacts on production workflows and society.
10 Key Moments in AI History
A concise technical review of AI history covering 10 pivotal milestones—from Dartmouth and perceptron to deep learning breakthroughs and the rise of large models.
Four Methods for Fine-Tuning Large Models
Technical overview of large-model fine-tuning and PEFT approaches, covering prompt/prefix tuning, P-tuning v2, AdaLoRA, adapter/LoRA methods and standard training workflow.
Popular LLM Inference Stacks and Setups
Survey of LLM inference stacks covering throughput, latency and cost; explains hardware constraints, KV cache, quantization, paged/grouped attention, and practical optimizations.
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.
Why GPUs Are Suited for AI: AI Server Supply Chain Analysis
Technical overview of AI servers, GPU/CPU architectures, training vs inference, compute demand and market estimates, including H100/A100 performance and China server market
Five Core Network Requirements for Large AI Models
Network requirements for large-model GPU training: RDMA-based bandwidth, ultra-low latency, stability, and automated deployment for scalable multi-GPU clusters.
How to Achieve a Trillion-Transistor GPU
Analysis of semiconductor advances enabling AI scale: 3D integration, CoWoS/HBM packaging, silicon photonics and energy-efficient trends toward trillion-transistor GPUs.
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.
VCS Verification Flow with VSO.ai
Overview of Synopsys VSO.ai integration into VCS and its AI-driven verification methods to accelerate coverage convergence, infer coverage, and reduce redundant regressions.