Aug. 05, 2025 –
Monolithic AI chips are just not viable, since they force trade-offs at every level, including thermal limits and reticle constraints.
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.
For AI inference, where data movement is the dominant power sink and architectural flexibility is crucial, this approach isn’t only attractive but also essential.
The semiconductor industry is already undergoing this shift. A growing ecosystem of chiplet suppliers now offers unique capabilities and interconnects, enabling flexible integration of components tuned for evolving AI workloads. At d-Matrix, we’ve pioneered a new AI inference architecture built on this foundation—delivering compact die sizes, enhanced yields, and cost efficiencies.
Our system is designed to serve both low-latency inference for interactive models and high-throughput compute for batch jobs, all on a modular silicon base...