Lecture Notes on Systolic Arrays and VLIW in Context of Machine Learning

Introduction

• VLIW (Very Long Instruction Word): Concept that needs reevaluation in the context of machine learning.
• Parallelism in Computation: Machine learning models can extract more parallelism.
• Reexamination of Concepts: Fundamental concepts should be revisited over time.

Systolic Arrays

• Importance: Fundamental and critical in computer architecture.
• Historical Context: Initially there were no implementations, now common in machine learning accelerators.
• Teaching Experience: Instructor emphasizes the importance of teaching these concepts early.

Seminar and Research Opportunities

• Bachelor Seminar: Rigorous seminar focusing on critical thinking and modern computer architecture topics.
• Research Opportunities: Encouragement for Bachelor students to engage in research; networking through seminars and direct communication.

Concepts in Computer Architecture

• Systolic Arrays: Execution model fundamental to machine learning workloads.
• Comparison with General Purpose Systems: Special purpose systems can be more efficient for specific applications.

Detailed Look at Systolic Arrays

• Execution Model: Data flows rhythmically through processing elements, similar to blood flow from the heart.
• Design Principles: Simple design, high concurrency, and efficient balance between computation and I/O.
• Programming Considerations: Data orchestration is key in maximizing computation and minimizing memory bandwidth usage.

Systolic Arrays in Machine Learning

• Convolutional Neural Networks (CNNs): Use of convolutions in image recognition and machine learning.
• Convolution Operations: Implemented efficiently using systolic arrays.

Applications and Generalization

• Specialized Accelerators: Used for both historical image processing and modern machine learning tasks.
• Matrix and Vector Multiplications: Efficiently computed using systolic arrays as shown in Google TPUs.
• Generalization of Systolic Arrays: Can be extended to more generalized execution models like stream processing.

Advantages and Disadvantages

• Advantages:
  • High efficiency and simple design.
  • Reduced need for frequent data fetching.
  • High concurrency and regular design.
• Disadvantages:
  • Limited to specific applications.
  • Challenging to program for irregular computations.

Modern Implementations

• Historical Implementations: Initial implementations at CMU for image processing tasks.
• Current Implementations: Google's TPU (Tensor Processing Unit) employs systolic arrays for machine learning.

Closing Remarks

• Encouragement to explore further readings and developments in the field of systolic arrays and machine learning accelerators.
• Future Lectures: Will cover SIMD (Single Instruction, Multiple Data) concepts in subsequent classes.

Note: Engagement in seminars and research is encouraged for deeper understanding and involvement in cutting-edge topics.