Vision Transformer Basics and Object Detection

Overview of Vision Transformer architectures and their use in object detection, covering encoder-decoder design, multi-scale fusion, DETR and Deformable DETR approaches.

Future Directions in Lightweight Deep Learning

Review of lightweight deep learning for resource-constrained devices: TinyML, quantization, architectures and deployment strategies for efficient inference.

AIoT Connecting AI to the Physical World

Explains how AIoT links AI and physical devices via IP-based networks and application layers like Matter, and deployment considerations for scalable device connectivity.

RDK X3 Tennis Ball Collector Robot

Autonomous tennis-ball collection and serving robot using YOLOv5 detection and SLAM/AMCL localization, with optimized path planning and hierarchical wheel control.

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.

Benchmarks for Harmlessness Evaluation in the LLM Era

Overview of FFT benchmark: a 2,116-instance evaluation of LLM harmlessness assessing factuality, fairness, and toxicity to reveal model harms and mitigation gaps.

What foundational devices do AI chips require?

Tsinghua's Future Chip Forum recap: Wei Shaojun outlines constraints for zettascale systems, device needs and prospects for 3D integration.

Practical Tricks for Reward Models

Technical overview of methods to improve reward model robustness for RLHF: quantify preference strength, flip/soften labels, apply adaptive margins, contrastive learning and MetaRM

What is an AI Edge Compute Box?

Comprehensive overview of an AI edge compute box: definition, operation, features, security, and industry use cases for real-time edge computing and computer vision.

Deep Learning Advances in Polarization Image Fusion

Comprehensive review of polarization image fusion and deep learning methods (CNN, GAN), traditional algorithms, datasets, applications, and future research directions.

Renesas RZ/V2L Pretrained AI Model

RZ/V2L MPU with DRP-AI overview and pretrained plant leaf disease classification model; runtime modes, hardware/software requirements, and inference performance.

F-Learning: Forget-then-Learn for LLM Knowledge Updates

F-Learning: a parameter-based fine-tuning paradigm that subtracts knowledge parameter deltas to forget outdated facts, then fine-tunes (LoRA or full-model) to update LLM knowledge.

Can LLMs Reason and Plan?

Analysis of recent research evaluating whether LLMs can plan or reason, showing limited autonomous planning and that apparent emergent capabilities stem from in-context learning.

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.

Passive Component Challenges and Solutions in AI

Overview of passive components for AI systems: material, architectural and process innovations for high-current power inductors and low-ESR polymer tantalum capacitors.

Resolution-Refutation Logic Reasoning for Large Language Models

GFaiR applies resolution-refutation over natural language to improve first-order logic reasoning in LLMs, boosting generalization and faithfulness with a validator.

CNN Image Classification Strategy

Explains how BagNets show ImageNet classification relies on local bag-of-features strategies, revealing CNN texture bias, patch-based evidence and robustness issues.

Impact of Irrelevant Retrieved Passages on LLM Outputs in RAG

Retrieval-augmented generation robustness analysis: semantically related but answer-irrelevant retrieved fragments and higher fragment counts degrade LLM accuracy and confidence.

Beginner's Guide to Intelligent Computing Centers

Overview of intelligent computing center architecture and operation: GPU clusters, high-speed storage and networking, distributed frameworks, intelligent OS, and AI access models.

Survey of LLM Inference on Tabular Data

Survey of table inference using large language models: tasks, datasets, methods (fine-tuning, in-context learning, tools), benchmarks and future research directions.