This image illustrates the evolution of computing architectures, comparing three major computing paradigms:
1. General Computing (Von Neumann Architecture)
- Traditional CPU-memory structure
- CPU and memory are separated, processing complex instructions
- Data and instructions move between memory and CPU
2. GPU Computing
- Collaborative structure between CPU and GPU
- GPU performs simple mathematical operations with massive parallelism
- Provides high throughput
- Uses new types of memory specialized for AI computing
3. PIM (Processing-in-Memory)
The core focus of the image, PIM features the following characteristics:
Core Concept:
- “Simple Computing” approach that performs operations directly within new types of memory
- Integrated structure of memory and processor
Key Advantages:
- Data Movement Minimization: Reduces in-memory copy/reordering operations
- Parallel Data Processing: Parallel processing of matrix/vector operations
- Repetitive Simple Operations: Optimized for add/multiply/compare operations
- “Simple Computing”: Efficient operations without complex control logic
PIM is gaining attention as a next-generation computing paradigm that can significantly improve energy efficiency and performance compared to existing architectures, particularly for tasks involving massive repetitive simple operations such as AI/machine learning and big data analytics.
With Claude
Lechuck Park 