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The case is made that barriers for new accelerators can be lowered by starting with a baseline AI accelerator, and adding minimal logic to support new operators demanded by new specialized domains, which leads to a versatile chip that can be manufactured at high volume and deployed for a range of popular applications.
Auditing AI systems in real-world settings requires a shift from static benchmarks to dynamic, uncertainty-aware monitoring of fairness and safety constraints.
