Learning Philosophy
Learn → Visualize → Interact → Experiment → Case Study → Challenge → Quiz → AI Tutor. Each AC module now has its own scientific/engineering figure and its own interactive experiment rather than sharing one generic diagram.
Beginner Modules AC-01 to AC-15
What Is a Computer Chip?
Begin with the physical idea of a semiconductor chip: a tiny piece of engineered material containing transistors, interconnects, logic, memory, and specialized circuits that together perform computation.
Inside a Modern Computer
Explore the major computing blocks inside a modern system and learn how CPU, GPU, NPU, RAM, storage, buses, and I/O cooperate rather than operating independently.
Understanding the CPU
Learn how a CPU executes instructions, why cores and clock frequency matter, and why general-purpose processors remain essential even in systems containing GPUs and NPUs.
Understanding the GPU
Understand why GPUs excel at highly parallel workloads and how thousands of similar operations can be organized across many execution units.
What Is an NPU?
Explore Neural Processing Units, tensor operations, TOPS, model inference, memory bandwidth, quantized arithmetic, and performance-per-watt in edge AI.
CPU vs GPU vs NPU
Compare three major processor classes and learn why intelligent systems are increasingly heterogeneous rather than relying on a single universal processor.
Memory, Cache & Storage
Learn how data is stored and moved through registers, cache, RAM, and persistent storage, and why memory hierarchy can dominate real computing performance.
What Is a System-on-Chip?
Build a simplified SoC by combining processing cores, accelerators, memory interfaces, communication blocks, security, and sensor I/O on one integrated platform.
Sensors + Intelligent Computing
Connect real-world sensing to intelligent computing and learn how cameras, microphones, LiDAR, biosensors, and industrial sensors generate data that must be sampled, conditioned, transported, and interpreted.
What Is Edge AI?
Understand why AI is increasingly executed close to sensors and users, reducing cloud dependence and enabling low-latency, privacy-sensitive, and connectivity-resilient applications.
How an AI Model Runs on a Chip
Follow an inference from raw input through preprocessing, tensor layers, memory transfers, activations, and output interpretation to understand what hardware actually does when 'AI runs'.
TOPS, FLOPS, Latency & Throughput
Learn how AI hardware performance is measured and why theoretical operations per second must be separated from application latency, throughput, utilization, precision, and efficiency.
Power, Heat & Cooling
Study how electrical power becomes heat, why chips throttle when thermal limits are reached, and how performance-per-watt influences edge devices, data centers, and product design.
AI Accelerators
Go inside specialized AI hardware and explore multiply–accumulate arrays, tensor engines, systolic dataflow, sparsity, quantization, and specialization.
Build Your First Virtual AI Computer
Integrate everything learned in AC-01 to AC-14 by designing a virtual intelligent computer with CPU, GPU, NPU, memory, sensors, power budget, and an AI workload.
Virtual Laboratories
The AC series provides foundations for later CPU, GPU, NPU, memory, Edge AI, SoC, FPGA, thermal, and AI Computer-on-Chip virtual laboratories.
Real-World Case Studies
Module cases cover smartphones, robotics, smart factories, edge AI, sensor systems, AI inference, accelerators, and complete intelligent-system design.
Roadmap
Beginner learning → Engineering simulations → Virtual laboratories → Real AI models → FPGA → Advanced AI-chip design → Research & innovation.