Our technology is built ground-up with a software-defined, data-driven system development approach.
AI’s growth has surged compute demand, with generative AI search costing nearly ten times more than standard search. Increased latency in searches or recommendation algorithms risks billions in losses, driving demand for efficient, specialized compute with new challenges.
To support growing compute demands and energy costs, computing is shifting to specialized solutions with chiplet integration for optimal systems. This increases complexity and risk.
Specialized computing platforms demand faster and more complex data interconnects, making it tougher to hit performance and latency KPIs, especially in multi-chip designs.
Larger, complex designs increase energy for processing and data movement, expanding silicon footprint and power, raising silicon and packaging costs.
The breakneck pace of AI innovation demands designers nail critical KPIs on the first silicon.
Transport is separate from protocol layers, minimizing wires and logic in building a unified fabric that supports coherent, non-coherent, and custom protocols for greatest efficiency with lowest cost and power.
Extensive analysis and optimization through software platform enables a fabric that delivers guaranteed performance on target workloads and can be algorithmically optimized for future workloads.
Software-driven development ensures the fabric is correct by construction, and deadlock free with traditional and formal validation, substantially reducing risk for system development.
Fabric channels in high performance silicon can take up to 20% of die area and power consumption
vs standard mesh iso-performance
Performance bottlenecks created by protocol bridges, fabric transit points and longer routes
bisection bandwidth
Need for numerous protocol crossing bridges and scenic routes for communication flows
system and workload dependent
Deadlocks, QoS and tradeoffs unclear during design development
per network, deadlock-free, QoS built-in
Limited number of topologies, performance not scalable
with wide variety of topologies
Performance issues slow down architecture, complexity challenges slow down implementation
and physically-aware implementation
