Architecture Micro-Architecture

Define product feature and capabilities and own the architecture for compute, memory, interconnect high-speed interface subsystems in the AI inference chip.

Collaborate with various software teams to co-optimize hardware features for AI workloads.

Collaborate with RTL designers to identify complex technical issues/risks. Review and guide RTL implementation, ensuring consistency with architectural intent and timing/power goals

Collaborate with Physical-design teams for Area/Floorplan refinement, Timing targets etc.

Define and document interface specifications, control/status logic, and pipeline structures.

Lead PPA analysis and trade-off discussions across RTL and architecture.

AI Workloads HW-SW Co-Design

Collaborate closely with various software to co-optimize hardware features for real-world AI inference workloads.

Incorporate considerations such as, quantization, sparsity, dataflow, scheduling,



memory bandwidth into architectural decisions.

Guide hardware features that improve programmability, debuggability, and long-term software scalability.

Modelling Analysis

Develop and maintain high-level architecture and performance models.

Use simulation and architectural models to guide RTL-level improvements.

Validate model predictions against RTL or emulation results and refine accordingly.

Solid understanding of AI inference workloads, dataflows, quantization, and memory/bandwidth bottlenecks in edge deployments

More information about NXP in India...

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📌 Sr Hardware ASIC Arch/Design Engineer (Hyderabad)
🏢 Kinara.ai
📍 Hyderabad