Rapid advances in artificial intelligence have re-energized the semiconductor industry, with numerous companies developing AI-specific silicon aimed at providing ever-higher levels of performance and power efficiency. But several critical challenges await chip and system designers as our industry strives to meet the relentless demands for more performance and better power efficiency, along with new usage models. Among the most critical of these challenges are memory system bottlenecks that have exacerbated the decades-old processor memory gap, creating an “AI-memory gap” that threatens the continued progress of AI silicon. Additional challenges are brought about by the difficulty of moving data at high speeds to AI accelerators, and the growing need for securing cloud-based infrastructures. In this talk I’ll discuss some of these challenges, as well as potential ways to support the continued progress of AI silicon.