Kuis terkait panduan · Keras Tingkat
Chip AI & Kuis Perangkat Keras
Evaluasi perangkat keras AI berdasarkan beban kerja, memori, presisi, dukungan perangkat lunak, overhead multi-perangkat, dan pengukuran kinerja bermanfaat yang dapat direproduksi.
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What should a team define before comparing AI hardware?
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Jawaban dan penjelasan muncul saat Anda mengikuti kuis.
- What should a team define before comparing AI hardware?
- The workload, such as training, interactive inference, or on-device processing
- Only the model parameter count
- A vendor’s peak operation count
- The model name
- Why does peak arithmetic performance alone not predict application speed?
- Peak figures include monitor refresh rate
- Memory movement, software, and other bottlenecks can dominate
- All workloads are compute-bound
- Arithmetic is unrelated to models
- What should be checked alongside compute capacity?
- Only the published weight-file size
- Only the arithmetic throughput at maximum utilization
- Memory capacity and bandwidth, including buffers and cached state
- Only the sum of memory across all devices
- What does the guide caution about memory spread across multiple devices?
- It always becomes one seamless pool
- It eliminates communication costs
- It guarantees model quality
- Aggregate memory is not automatically one simple pool
- What should a team evaluate when using lower-precision numerical formats?
- Accuracy changes on the intended model and task
- Only whether model files became smaller
- Only whether the numerical format is advertised
- The peak operation rate without running the model
- When is an advertised hardware capability actually used?
- Whenever a chip has a high peak score
- When hardware support, kernels, and the framework use it
- Only when a benchmark says so
- When listed in a brochure
- Which measurements does the guide recommend for reproducible hardware comparisons?
- Only peak arithmetic
- Only a vendor demo
- Latency, throughput, power, and cost per useful task with stated settings
- Processor count alone
- One billion parameters stored at 16 bits each takes about how much weight storage in the worked example?
- 200 MB for the whole system
- 20 GB including all runtime buffers
- 2 GB including activations and cache
- 2 GB for weights alone; other memory needs are excluded