What is Hardware Aware Optimization in ML?

Definition
Hardware-aware optimization designs ML models knowing their deployment target: GPU type, memory limits, latency budgets. Instead of training the best model then squeezing it onto hardware, you build constraints into the design process from the start.
Why Hardware Constraints Matter
A model optimized for V100 GPUs may run 3x slower on T4s due to different memory bandwidth and tensor core generations. Mobile chips have 10-100x less compute than cloud GPUs. Edge devices often lack FP16 support or have unusual memory hierarchies. Building without hardware awareness leads to: models too slow for latency SLAs, models too large for device memory, expensive rewrites after discovering constraints late.
The Traditional Approach Fails
Traditional workflow: design model → train → optimize for deployment. This fails because: architectural choices made during design may be fundamentally incompatible with target hardware; post-hoc optimizations can only recover 20-30% of potential speedup; you discover constraints too late when changing architecture is expensive. Hardware-aware approach: define hardware constraints first → search for architectures that fit → train within constraints.
Key Constraint Types
Latency: maximum inference time (e.g., 10ms for real-time). Throughput: minimum requests per second. Memory: model size + activation memory must fit device RAM. Power: critical for mobile/edge (watts consumed). Cost: cloud inference cost per 1000 requests. Each constraint shapes architectural choices differently.

💡 Key Takeaways

✓Hardware-aware optimization builds deployment constraints into model design, not post-hoc optimization

✓V100-optimized models may run 3x slower on T4s; mobile has 10-100x less compute than cloud GPUs

✓Traditional approach recovers only 20-30% of potential speedup; constraints discovered too late

✓Key constraints: latency (ms), throughput (QPS), memory (RAM), power (watts), cost ($/1000 requests)

✓Define hardware constraints first, then search for architectures that fit

📌 Interview Tips

1Explain why hardware-first design outperforms optimize-later approach with specific numbers (20-30% recovery)

2List the five constraint types (latency, throughput, memory, power, cost) to show systematic thinking

3Mention V100 vs T4 performance gap to demonstrate real-world awareness of GPU generations

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