The most turbulent flight routes in North America are over Colorado, where the prevailing winds from the west barrel into the high peaks of the Rockies and tumble onto the High Plains below. One morning this fall, on a stubbly brown field in Boulder, a glider pilot named Dan Swenson stared up at the sky and shook his head. A vast, lens-shaped cloud hung above us like an alien mother ship. It stretched from the foothills of the Front Range, in the west, to the Laramie Mountains, in the north, its pale upper reaches darkening to a gunmetal gray along the bottom. “So, what’s with this?” he said. He glanced over at Jordon Griffler, the scraggly young pilot who would tow Swenson’s glider into the sky with his single-prop plane. Griffler shrugged and took a bite of a bagel. “You can ride that all the way to Wyoming,” he said. Swenson shook his head again: “Holy cow!”
Most deep learning frameworks are built for flexibility. They handle dynamic graphs, varying batch sizes, and a multitude of layer types. Talos takes the opposite approach. It strips away the runtime, the scheduler, and the operating system overhead to expose the raw compute capability of the FPGA. By implementing the entire inference pipeline in SystemVerilog, we achieve deterministic, cycle-accurate control over every calculation.
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