Interview Questions

Tests analog circuit optimization for noise and power-supply rejection in TETRAMEM systems.

Tests systems thinking for memory bandwidth, tiling, and data movement in constrained in-memory compute.

Tests analog design reasoning and trade-off selection for mixed-signal IC performance.

Tests embedded driver design, timing control, and reliability under hard real-time constraints.

Tests compiler architecture knowledge and ability to connect MLIR to practical compilation flows.

Tests algorithm design for quantization and mapping under hardware constraints in in-memory compute.

Tests debugging methodology for concurrency bugs and memory management issues in embedded C++.

Tests your understanding of backprop math and how training is affected by hardware limitations.

Tests your end-to-end execution, decision-making, and ability to translate process to a new context.

Tests your engineering practices for reproducibility, collaboration, and reliable experimentation.

Tests your ability to balance accuracy, latency, and hardware efficiency for edge deployment.

Tests your ability to design mapping algorithms that fit crossbar constraints while preserving model quality.

Tests your understanding of analog hardware effects and how they impact model mapping and performance.

Tests your skill in reducing inference memory through architecture and implementation choices.

Tests your ability to choose and implement PTQ vs QAT for transformer accuracy and deployment constraints.

Tests your ability to implement custom training/inference simulation hooks using PyTorch autograd.

Tests your systems understanding of LLM inference bottlenecks and the role of in-memory computing.

Tests your ability to close the sim-to-hardware gap with robust validation and debugging.

Tests your understanding of memory technologies and their implications for accelerator design and performance.