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.

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

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