Transitioning from FPGA to ASIC for Your AI Chip: Key Considerations

Transitioning from FPGA to ASIC in the development of your AI chip requires careful consideration and strategic planning. This journey involves multiple complexities and challenges that demand a nuanced understanding of both the FPGA and ASIC environments. Here's a comprehensive guide on what you should know as you make this pivotal shift.

Strategic Planning for AI Accelerator Development

Diverse Architectural Demands:

Proof-of-Concept Tensions:

Initiating the process with a proof-of-concept or a verification platform in FPGAs introduces immediate challenges. Balancing the expectations of architects, software teams, and market pressures requires meticulous planning to avoid the proof of concept taking an unplanned direction.

Three-Way Tug-of-War:

Architectural Fidelity vs. Performance Optimization:

Architects seek fidelity to microarchitecture for algorithm efficiency, while the software team urges FPGA design optimization for enhanced performance. Navigating this three-way tug-of-war is essential for a successful FPGA implementation.

ASIC Migration Considerations

Performance, Power, and Area Profiles:

Shifting to an ASIC for volume production necessitates acquiring the desired performance, power, and area profiles. The challenge lies in aligning the ASIC design with the original architecture while incorporating optimizations for the chosen ASIC process and IP libraries.

Architectural Features and FPGA Implementation Challenges

Pervasive Parallelism:

FPGA Implementation Challenges:

Parallel processing, fundamental to AI accelerators, presents challenges in FPGA implementation. The need for human intervention in floor planning and placement is crucial, focusing on FPGA resource availability rather than strictly adhering to the original architecture.

Reduced-Precision Arithmetic and Design Alterations:

Conflict in Design Choices:

Architectural preferences for reduced-precision arithmetic may clash with FPGA teams favoring different approaches. Balancing these conflicting choices during FPGA design can impact the conversion process.

ASIC Conversion Considerations

Mechanical Steps vs. Design Intent Understanding:

FPGA-to-ASIC Conversion:

While FPGA-to-ASIC conversion might seem mechanical, understanding the original design intent becomes crucial. The conversion team must decide whether to retain FPGA-specific optimizations or revert to the original architecture, synthesizing code judiciously.

RAM Utilization Challenges:

Memory Utilization in AI Accelerators:

Memory utilization, a critical aspect of AI accelerators, poses challenges in FPGA implementation. Choices during the transition from FPGA to ASIC involve deciding between FPGA-specific modifications or reverting to the original architecture for optimal RAM utilization.

Managing Multiplicity in FPGA Designs:

Dealing with Multiple FPGAs:

Designs exceeding the capacity of a single FPGA require distribution across multiple chips. The communication method between these chips influences the conversion process, necessitating a thorough understanding of the original architecture to address any alterations made during the FPGA stage.

Close Collaboration for Successful Conversion

Clock Architecture Exploration:

Freedom in ASIC Clock Architecture:

The ASIC's freedom from FPGA constraints allows exploration of the original architecture's theory of operation. Leveraging opportunities for local clock regions, gating, and frequency shifting becomes essential for optimizing the ASIC design.

Built-in Self-Tests and Supply Chain Optimization:

Exploiting Parallel Architecture:

An experienced conversion team can exploit the parallel architecture for efficient built-in self-tests, extending optimization beyond the ASIC design and influencing the supply chain. This can significantly reduce test costs, enhancing overall unit cost efficiency.

Conclusion

Effectively moving from FPGA to ASIC for your AI chip requires a holistic understanding of architectural demands, FPGA implementation challenges, and strategic considerations during the conversion process. A collaborative and informed approach, considering the intricacies of both FPGA and ASIC environments, will pave the way for a successful transition, ultimately achieving the desired power, performance, and area objectives for your AI chip.