Date: Nov 14, 2023
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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
CPLD CoolRunner XPLA3 Family 750 Gates 32 Macro Cells 95MHz 0.35um Technology 3.3V 48-Pin CSBGA
CPLD CoolRunner XPLA3 Family 750 Gates 32 Macro Cells 95MHz 0.35um Technology 3.3V 44-Pin PLCC
FPGA Spartan-II Family 15K Gates 432 Cells 263MHz 0.18um Technology 2.5V 144-Pin TQFP EP
FPGA Spartan-II Family 15K Gates 432 Cells 263MHz 0.18um Technology 2.5V 144-Pin TQFP
CPLD CoolRunner XPLA3 Family 750 Gates 32 Macro Cells 95MHz 0.35um Technology 3.3V 44-Pin VQFP