Tens of millions of items of clothing are thrown away each year, and the synthetic materials often used by fast fashion retailers are among the hardest to recycle.
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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Phase 0 is where we initialize all our sprite data for the frame, as well as set up the character graphics for the logo display. This is straightforward though it does need a few loops.
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