In-Memory Compute Chips for ML Inference

System-level evaluation of compute-in-memory (CiM) for accelerating GEMM in ML inference: compares analog vs digital CiM, cache-level integration, and optimal dataflows.

Effective Power Management for AI Data Centers

Technical overview of data center power management: hybrid capacitors, low?ESR components, precision resistors and wireless monitoring to improve efficiency and reliability.

Understanding Generative Adversarial Networks (GANs)

Explains how generative adversarial networks (GANs) work and shows step-by-step training of a PyTorch GAN on MNIST, including network design and training loop.

Beginner's Guide to Training Large Language Models

Technical guide to scaling LLM training: analyzes memory usage, gradient accumulation, ZeRO, and tensor/data parallelism to improve throughput and GPU utilization.

Building a Multilingual Open-Ended QA Dataset

OMGEval presents an open-source multilingual open-ended QA benchmark (804 Chinese prompts) localized from AlpacaEval, using Text-Davinci-003 baseline and GPT-4 evaluation.

Length Extrapolation with Positional Encoding

Survey of length extrapolation approaches analyzing positional encoding methods and their effects on extending model context windows for long-sequence inference.

Deploy DeepSeek-R1 on RK3588 Development Board — Part 1

Technical guide to porting DeepSeek-R1 onto RK3588-based edge hardware: model conversion, cross-compilation, board deployment, and measured CPU, memory, and NPU performance.

Random Forest: What It Is and How It Works

Technical overview of random forest algorithms, bagging, bias-variance tradeoffs, and GPU-accelerated implementations (RAPIDS) for faster model training.

AI Chip Ecosystem: Analysis and Future Outlook

Analysis of the AI chips ecosystem: technical, commercial, and strategic factors shaping developer affinity, multi?generation platform continuity, and ecosystem influence.

Key Factors in Designing an AI Chip

Review of neural network quantization and numeric formats, covering floating vs integer, block floating point, logarithmic systems, and inference vs training trade-offs.