Chiplets Are The New Baseline for AI Inference Chips
Monolithic AI chips are just not viable, since they force trade-offs at every level, including thermal limits and reticle constraints.
By Sid Sheth, Founder & CEO of d-Matrix
EETimes | August 5, 2025
AI has moved from proof-of-concept to production at scale, and inference, not training, is where the real operational and economic pressure lies. Whether you’re powering conversational agents, orchestrating industrial automation, or deploying AI at the edge, the cost of inference now dominates the AI lifecycle.
Yet many systems still rely on monolithic chip architectures that are fundamentally misaligned with the realities of inference workloads.
The result? Wasted energy. Inflated costs. Underutilized silicon.
Chiplet-based architectures offer a way out. By partitioning a system into tightly integrated, functional modules—compute, memory, interconnect, and control—chiplets enable better yield, more efficient packaging, and faster system evolution.
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