🪴 Avril Chen

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Untether AI Interview

Two interviews with them on 01-27-2025.

  • First one at 1:30pm for Computer Kernel SWE position
  • Second one at 4:30pm for System SWE position

Prep w/ Deepseek

Here’s a breakdown of potential interview questions and model answers tailored to the key focus areas for both roles:

1. Technical Optimization

Q: How would you optimize a matrix multiplication kernel for a custom AI accelerator? A:

  • Step 1: Algorithm Choice: Use tiling (blocking) to fit submatrices into cache, reducing memory bandwidth.
  • Step 2: Vectorization: Leverage SIMD intrinsics (e.g., AVX-512) to process multiple elements in parallel.
  • Step 3: Loop Unrolling: Manually unroll loops to reduce branch overhead.
  • Step 4: Memory Alignment: Use aligned_alloc to ensure memory alignment for SIMD instructions.
  • Step 5: Parallelization: If the accelerator supports multithreading, split work into threads with minimal synchronization.
  • Example: “In a recent project, I optimized a GEMM kernel by tiling matrices into 64x64 blocks and using AVX2 intrinsics, achieving a 3x speedup on CPU.”

Q: What’s the difference between malloc and aligned_alloc, and why does alignment matter? A:

  • malloc: Allocates memory with no alignment guarantees (may start at any address).
  • aligned_alloc: Guarantees alignment to a specified boundary (e.g., 64 bytes for AVX).
  • Why Alignment Matters: Misaligned memory causes cache line splits, leading to slower SIMD loads/stores.

Q: Explain how you’d implement an efficient ReLU activation layer in C++. A:

  • Naive Approach: Loop over elements and apply output[i] = max(0, input[i]).
  • Optimization:
    • Use SIMD intrinsics (e.g., _mm256_max_ps for AVX) to process 8 floats at once.
    • Align input/output buffers to 32-byte boundaries.
    • Unroll loops to reduce branch overhead.
    • Example: “I vectorized a ReLU layer using AVX, reducing latency by 60% in a CNN inference pipeline.”

2. Math & AI Concepts

Q: Explain the math behind a transposed convolution layer A:

  • Purpose: Used for upsampling (e.g., in segmentation networks).
  • Operation: Each input pixel is multiplied by a kernel and added to multiple output pixels (opposite of regular convolution).
  • Stride: Controls spacing between kernel applications (e.g., stride=2 doubles output size).

Q: How does a convolution layer differ from a fully connected layer in terms of computation? A:

  • Convolution: Shares weights across spatial locations (sliding window), reducing parameters and leveraging locality.
  • Fully Connected: Each neuron connects to all inputs, leading to dense matrix multiplication.

3. Low-Level Debugging

Q: How would you debug a kernel that produces correct results on small inputs but fails on large inputs? A:

  • Step 1: Check for memory overflows (e.g., buffer sizes, indexing errors).
  • Step 2: Use Valgrind or AddressSanitizer to detect leaks/out-of-bounds access.
  • Step 3: Profile with tools like perf to identify cache thrashing or branch mispredictions.
  • Example: “I once fixed a matrix kernel failing on large inputs by replacing row-major indexing with a tiled approach to avoid cache misses.”

1. Systems Design & Debugging

Q: Design a userspace API for a PCIe-based AI accelerator. What considerations are critical? A:

  • Key Components:
    1. Memory Management: Expose DMA functions for zero-copy transfers between host and device.
    2. Synchronization: Use mutexes or semaphores to handle concurrent access.
    3. Error Handling: Return status codes for PCIe errors or hardware faults.
    4. Abstraction: Hide hardware specifics behind simple functions like accelerator_run_kernel().
  • Example: “I’d model the API after CUDA’s runtime API, with functions for memory allocation, kernel launch, and event-based synchronization.”

Q: How would you debug a kernel module that crashes during chip bring-up? A:

  • Step 1: Check dmesg logs for panic messages or stack traces.
  • Step 2: Use kgdb or JTAG debugging to inspect registers/memory.
  • Step 3: Isolate the issue by disabling features (e.g., DMA, interrupts) and re-enabling incrementally.
  • Example: “During an FPGA project, a kernel panic was traced to an incorrect interrupt request (IRQ) mapping using logic analyzer traces.”

Q: Explain how DMA improves performance in data transfers. A:

  • DMA (Direct Memory Access): Allows hardware to transfer data between memory and peripherals without CPU involvement.
  • Benefit: Frees the CPU to perform other tasks while large transfers (e.g., PCIe packets) occur in parallel.

2. OS & Hardware Interaction

Q: How does a Linux device driver interact with userspace? A:

  • Syscalls: Userspace uses open(), read(), ioctl() to interact with the driver.
  • Kernel Interfaces: The driver implements file_operations struct (e.g., .read, .write).
  • MMAP: Userspace can map device memory directly for low-latency access.

Q: What’s the difference between a mutex and a semaphore?
A:

  • Mutex: Binary lock for mutual exclusion (e.g., protecting a shared hardware register).
  • Semaphore: Generalizes mutex with a counter (e.g., limiting concurrent accesses to a resource pool).

3. Behavioral Questions

Q: Tell me about a time you debugged a complex hardware/software interaction issue. A (STAR Format):

  • Situation: A sensor driver intermittently returned corrupted data.
  • Task: Identify if the issue was in software (driver) or hardware (sensor).
  • Action:
    • Wrote a test firmware to bypass the driver and read sensor registers directly.
    • Used a logic analyzer to capture SPI bus transactions.
  • Result: Discovered a timing violation in the driver’s clock configuration and fixed it by adjusting delays.

Q: Describe a project where you collaborated with hardware engineers.
A (STAR Format):

  • Situation: Developed a driver for a custom FPGA-based accelerator.
  • Task: Ensure the driver met latency requirements for real-time processing.
  • Action:
    • Worked with hardware engineers to refine the register map and interrupt scheme.
    • Co-designed a DMA engine to minimize CPU overhead.
  • Result: Achieved 10 Gbps throughput with <1ms latency.

Final Tips

  • Kernel Role: Emphasize performance metrics (e.g., “reduced latency by X%”) and math/optimization keywords (SIMD, cache blocking).
  • Systems Role: Highlight reliability (error handling, testing) and tools (GDB, logic analyzers, Wireshark).
  • Both Roles: Use Untether AI’s terminology (“at-memory computing,” “data movement bottleneck”) to show alignment with their mission.

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