Power & Precision Tradeoffs

Section goal

A practical guide to numeric format choices, quantization strategy, and thermal-power control loops that determine real accelerator efficiency at target model quality.

How to study this section

1. Start with each topic hub and restate the mechanism in your own words.

2. Use reports and debug pages to separate symptoms from root causes.

3. Practice worked examples and interview drills under fixed metadata.

4. Close with checklist and silicon impact before making release claims.

Topics

1. int8-fp16-bf16-fp8/ - INT8, FP16, BF16, and FP8 Format Selection

2. quantization-aware-design/ - Quantization-Aware Design Across Model and Hardware

3. perf-per-watt-optimization/ - Performance-per-Watt Optimization Workflow

4. thermal-and-dvfs-accelerator/ - Thermal Management and DVFS for Accelerators

Related topics

• AI Accelerator course hub

AI accelerator deep dive

Precision and DVFS policy must be co-designed with quality guardrails and thermal behavior.

Concept diagram

PRECISION-POWER LOOP

numeric format choice -> throughput and energy
         + thermal state and DVFS policy -> sustained SLA

Metric graph

PERF/W TRADE

INT8 efficiency      █████████
BF16 stability       ██████
thermal clamp risk   ████

Metrics and artifacts to collect

• precision-mode mix

• perf-per-watt trend

• thermal clamp frequency

• quality regression monitor

Mini case study

Switching to lower precision improved nominal throughput, but thermal clamp cycles reduced sustained gains.

Debug branches

• Validate quality guardrails by slice

• Correlate thermal events to latency tails

• Audit precision fallback behavior

Senior review question

Ask: which first-principles bottleneck class explains the symptom, and what artifact proves it reproducibly?