This content originally appeared on DEV Community and was authored by Arvind Sundara Rajan
Real-Time AI at the Edge: Dynamically Updating Neural Networks on FPGAs
Imagine trying to process a firehose of data, like the torrent of information from a high-speed sensor. The challenge? Getting actionable insights now, not later, without breaking the bank on transmission and storage. What if you could adapt your AI model on the fly, right there on the device, without a complete system reboot?
The core concept enabling this is a framework for deploying neural networks to FPGAs with the unique capability of dynamically updating model weights while the system is running. Forget lengthy re-synthesis cycles. This allows for adaptive learning scenarios directly on the edge, enabling real-time responses to changing conditions.
Think of it like tuning a radio. Instead of rebuilding the entire radio every time you want a different station, you simply adjust the dial (weights) to get the signal you want. Here's a simplified representation of how you might update weights using a control interface:
This content originally appeared on DEV Community and was authored by Arvind Sundara Rajan
Arvind Sundara Rajan | Sciencx (2025-09-02T20:06:29+00:00) Real-Time AI at the Edge: Dynamically Updating Neural Networks on FPGAs by Arvind Sundararajan. Retrieved from https://www.scien.cx/2025/09/02/real-time-ai-at-the-edge-dynamically-updating-neural-networks-on-fpgas-by-arvind-sundararajan/
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