ASIC & FPGA Optimization for AI and HPC

This course provides a comprehensive exploration of optimization techniques specifically tailored for Application-Specific Integrated Circuits (ASICs) and Field-Programmable Gate Arrays (FPGAs) in the context of Artificial Intelligence (AI) and High-Performance Computing (HPC) applications. It delves into the unique challenges and opportunities presented by these hardware platforms when accelerating computationally intensive workloads. The course aims to equip participants with the knowledge and skills necessary to design and implement highly efficient and performant AI and HPC systems using ASICs and FPGAs.

Course Objectives

• Understand the fundamental principles of ASIC and FPGA architectures.
• Master advanced optimization techniques for AI and HPC workloads on ASICs and FPGAs.
• Learn how to profile, analyze, and optimize performance bottlenecks in hardware designs.
• Explore various design space exploration methodologies for achieving optimal performance and power efficiency.
• Gain practical experience in implementing and evaluating optimized designs using industry-standard tools and methodologies.

Course Content

Introduction to ASIC and FPGA Architectures

• Overview of ASIC design flow: from specification to fabrication.
• Introduction to FPGA architectures: logic blocks, routing resources, and memory elements.
• Comparison of ASIC and FPGA platforms: trade-offs in performance, power, cost, and flexibility.
• Emerging trends in ASIC and FPGA technology.

Optimization Techniques for AI and HPC

• Dataflow optimization: maximizing data reuse and minimizing memory access.
• Pipelining and parallel processing: exploiting parallelism to improve throughput.
• Loop unrolling and tiling: enhancing data locality and reducing loop overhead.
• Custom instruction set extensions: tailoring hardware to specific AI and HPC algorithms.
• Memory hierarchy optimization: designing efficient memory systems for high bandwidth and low latency.
• Approximate computing: leveraging approximations to reduce power consumption and improve performance.

Performance Profiling and Analysis

• Hardware performance counters: monitoring key performance metrics such as clock cycles, memory accesses, and cache misses.
• Profiling tools and techniques: identifying performance bottlenecks in hardware designs.
• Performance modeling and simulation: predicting the performance of different design options.
• Power analysis and optimization: reducing power consumption without sacrificing performance.

Design Space Exploration

• Automated design space exploration: using algorithms to search for optimal design configurations.
• High-level synthesis: automatically generating hardware designs from high-level descriptions.
• Hardware/software co-design: partitioning functionality between hardware and software to maximize performance and efficiency.
• Constraint-driven optimization: optimizing designs to meet specific performance, power, and area constraints.

Implementation and Evaluation

• Using industry-standard tools for ASIC and FPGA design: synthesis, place-and-route, and verification.
• Implementing optimized designs on target hardware platforms.
• Evaluating the performance and power consumption of implemented designs.
• Comparing different optimization techniques and design choices.
• Debugging and troubleshooting hardware designs.

Advanced Topics

• High-bandwidth memory (HBM) integration: leveraging HBM for memory-intensive applications.
• Network-on-chip (NoC) design: building scalable and efficient communication networks for multi-core ASICs and FPGAs.
• Security considerations for AI and HPC hardware: protecting against side-channel attacks and other security vulnerabilities.
• Emerging architectures for AI and HPC acceleration: neuromorphic computing, quantum computing, and other novel approaches.

Benefits of Taking This Course

• Develop expertise in optimizing ASICs and FPGAs for AI and HPC applications.
• Gain a competitive edge in the rapidly growing field of hardware acceleration.
• Enhance your ability to design and implement high-performance, energy-efficient systems.
• Improve your problem-solving skills in the context of complex hardware design challenges.
• Expand your knowledge of cutting-edge technologies and trends in AI and HPC hardware.
• Learn how to effectively utilize industry-standard tools and methodologies for ASIC and FPGA design.