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LLM inference spends up to 97% of its time just *preparing* memory, but offloading that work to an FPGA can more than double inference speed.
ARLArena reveals the hidden instability of agentic RL, offering a path to more reliable LLM-based agents via a novel stable policy optimization method (SAMPO).
Democratizing hardware design and enabling next-generation hardware systems requires strategic NSF investment in AI/EDA collaboration, foundational AI, data infrastructure, and workforce development.
