Search papers, labs, and topics across Lattice.
4
0
9
9
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.
Forget scaling compute – the future of AI hinges on a 1000x leap in energy efficiency via tight AI+Hardware co-design over the next decade.
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.
