Solve SoC Bottlenecks with Smart Local Memory in AI/ML Subsystems
In today’s disaggregated electronics supply chain the (1) application software developer, (2) the ML model developer, (3) the device maker, (4) the SoC design team and (5) the NPU IP vendor often work for as many as five different companies. It can be difficult or impossible for the SoC team to know or predict actual AI/ML workloads and full system behaviors as many as two or three years in advance of the actual deployment. But then how can that SoC team make good choices provisioning compute engines and adequate memory resources for the unknown future without defaulting to “Max TOPS / Min Area”?
There has to be a smarter way to eliminate bottlenecks while determining the optimum local memory for AI/ML subsystems.
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