What are the challenges for implementing ml models on FPGA?
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Implementing machine learning (ML) models on Field Programmable Gate Arrays (FPGAs) presents several challenges. One major challenge is the complexity of adapting existing clocked FPGAs to handle asynchronous ML hardware. Additionally, the need for extensive simulations during ML accelerator implementation, especially for asynchronous designs, poses a significant obstacle due to the limitations of current commercial EDA tools. Furthermore, the requirement for large amounts of data for training ML models, the inability to predict performance in new environments, and the risk of overfitting with limited samples hinder efficient FPGA-based ML implementation. These challenges highlight the necessity for innovative approaches to address issues related to connectivity, privacy, security, stability, and energy efficiency in FPGA-based ML systems.
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| Papers (5) | Insight |
|---|---|
| Challenges for implementing ML models on FPGA include the need to map critical timing constraints while maintaining delay-insensitivity properties, especially in asynchronous designs like the Tsetlin machine. | |
01 Oct 2022 | Challenges for implementing ML models on FPGA include data inefficiency, environment-specific training, and overfitting. LEAPER proposes transfer learning to address these limitations effectively. |
| Challenges for implementing ML models on FPGA include complex computation tasks, subtask scheduling, and multi-step thinking, as discussed in the paper on LLMs for wireless communication system development. | |
| Challenges for implementing ML models on FPGA include the need for extensive simulations, adapting clocked FPGAs for asynchronous designs, and addressing timing constraints in circuits efficiently. | |
| Challenges for implementing ML models on FPGA include adapting hardware for model changes, optimizing performance, and managing data transfer efficiently without requiring frequent re-synthesis or reconfiguration. |
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