As we explored in the previous post (Systolic Array), while powerful Processing Elements (PEs) are essential, the primary concern for a system architect is: "How can we supply data seamlessly and cost-effectively?"
When designing an NPU at a low-power AI semiconductor startup, you stake your life on the goal of "achieving maximum performance with minimum power."
Designing NPU (Neural Processing Unit) architectures at a low-power AI semiconductor startup constantly reminds me of a harsh truth:
When designing an NPU for low-power AI semiconductor edge devices, you often find yourself spending more time fighting the EDA tools than writing actual RTL.
When mapping a low-power AI semiconductor architecture onto an FPGA, one of the very first hurdles you encounter is 'Memory Design'.
When an RTL engineer analyzes the synthesis results, he or she may find that the same AND2 gate has a subtly different name.
