Computer Vision Inference Throughput Benchmarking Methods

Overview of Technical Issues:

The benchmarking framework insufficiently measures the inference engine's true throughput because current methods fail to account for end-to-end pipeline behavior, warm-up effects, data transfer overhead, and realistic workload variability, resulting in misleading performance metrics that don't reflect production deployment scenarios; the goal is to establish comprehensive benchmarking methods that accurately capture realistic throughput under actual operating conditions.

Solution directions generated for this problem

Problem Direction 1 :

ImproveMeasurement coverage completeness
VS
ConstraintImplementation complexity

Inspiration 1 : Cross-domain reference

Application Principle: #1 Segmentation
Cross-domain applicability Assess applicability
Techniques and apparatuses for multiplexing schemes for millimeter wave downlink single carrier waveforms
Innovative Solution Refine solution

Modular self-contained benchmark instrumentation units with independent lifecycle management

Decompose benchmark into independent modules
How to solve :
  • Divide comprehensive benchmark framework into five independent modules: inference core (measures steady-state throughput), warm-up detector (monitors performance stabilization via 5% variance threshold over 10-sample window), data transfer profiler (tracks I/O overhead using timestamp injection), workload generator (replays production traces), and statistical analyzer (computes percentile metrics)
  • each module operates as standalone executable with standardized JSON input/output interfaces, enabling independent development, testing, and version control
  • implement composition orchestrator using declarative YAML configuration files that specify which modules to activate per benchmark profile—developers select "quick" profile (inference module only, 2-minute runtime), "standard" profile (inference + warm-up, 8-minute runtime), or "comprehensive" profile (all five modules, 45-minute runtime)
  • each module maintains <500 lines of code with unit test coverage ≥85%, ensuring maintainability
  • modules communicate via message queue middleware (e.g., Redis Streams) with schema validation at boundaries, preventing integration errors
Expected Effect : Codebase complexity +40% vs monolithic but maintenance effort -60%; module reuse rate 73%; profile switching <30 seconds
Risk Control :
  • interface schema drift between modules
  • message queue latency affecting accuracy
  • module version incompatibility

Problem Direction 2 :

ImproveMeasurement accuracy of realistic conditions
VS
ConstraintBenchmark execution time

Inspiration 1 : Cross-domain reference

Application Principle: #10 Preliminary action
Cross-domain applicability Assess applicability
Dormant mode measurement optimization
Innovative Solution Refine solution

Adaptive warm-up detection with pre-stabilized measurement windows

Pre-stabilize system before measurement
How to solve :
  • Implement automated warm-up detection monitoring performance variance in real-time — when coefficient of variation drops below 5% across 10 consecutive inference cycles, system is deemed stable and measurement window begins immediately, eliminating fixed warm-up overhead
  • Deploy dual-phase execution protocol — Phase 1 runs variable-duration warm-up (typically 30-90 seconds) with continuous latency and throughput monitoring at 100ms intervals until stabilization criteria met, Phase 2 executes fixed-duration measurement (3-5 minutes) capturing only steady-state metrics
  • Integrate workload trace replay engine that loads pre-recorded production request patterns (batch size distributions, inter-arrival times, query complexity profiles) from compressed trace files, replaying realistic variability without regenerative overhead, reducing setup time by 65%
Expected Effect : Measurement accuracy <10% deviation, execution time reduced 40-55% vs full-cycle benchmarks
Risk Control :
  • variance threshold miscalibration causing premature measurement
  • trace file representativeness degradation over time
  • storage overhead for production pattern libraries

Problem Direction 3 :

ImproveMetric representativeness of production scenarios
VS
ConstraintComputational resource consumption

Inspiration 1 : Cross-domain reference

Application Principle: #2 Taking out
Cross-domain applicability Assess applicability
Apparatus and method for signaling system information
Innovative Solution Refine solution

Production trace replay benchmark with canonical workload extraction

Extract canonical workload patterns from production to minimize test scenarios
How to solve :
  • Analyze production logs using statistical clustering (k-means on request size, inter-arrival time, batch size vectors) to identify 5-7 canonical patterns covering 90% behavior distribution
  • validate coverage via Kolmogorov-Smirnov test (p>0.05 threshold)
  • Capture each canonical pattern as replay trace files (JSON format: timestamp, request_size, batch_id) storing only pattern signatures (100-500 KB per trace) instead of full request payloads, enabling deterministic replay without regenerating workloads
  • Execute benchmarks sequentially through canonical traces on standard hardware (16GB RAM, 8 vCPU), measuring end-to-end throughput including warm-up (detect stabilization via coefficient of variation <5% over 20-sample window) and data transfer overhead with per-stage latency breakdown (preprocessing, inference, postprocessing) recorded at millisecond precision
Expected Effect : Resource consumption -70%, representativeness maintained at 90% production coverage, benchmark completes in 25-35 minutes
Risk Control :
  • clustering algorithm fails to capture rare critical patterns
  • trace replay timing drift from production behavior
  • canonical pattern set requires periodic re-extraction as workload evolves

Problem Direction 4 :

ImproveMeasurement coverage completeness
VS
ConstraintMust not deteriorate

Inspiration 1 : Cross-domain reference

Application Principle: #10 Preliminary action
Cross-domain applicability Assess applicability
Information on reference signal structure for neighbouring cell measurements
Innovative Solution Refine solution

Pre-stabilization benchmark protocol with automated warm-up detection

Separate warm-up from measurement window
How to solve :
  • Implement automated warm-up phase detection — monitor throughput variance across 10-second sliding windows, trigger measurement phase when coefficient of variation drops below 5% for three consecutive windows, ensuring steady-state capture without manual timing
  • Execute pre-stabilization protocol — load model weights, initialize memory pools, pre-fetch first 500 data batches, run 50 inference cycles before measurement starts, eliminating cold-start artifacts from reported metrics
  • Apply dual-mode benchmark execution — development mode runs 2-minute inference-only tests on code commits for rapid feedback (±15% accuracy), nightly validation mode executes full 45-minute end-to-end pipeline tests with automated warm-up detection (<10% deviation from production)
Expected Effect : Execution time reduced 60% vs continuous comprehensive measurement; production deviation <10%; development iteration preserved at 2-3 minutes
Risk Control :
  • variance threshold may miss oscillating warm-up patterns
  • pre-stabilization resource allocation conflicts with concurrent workloads
  • dual-mode synchronization gaps cause metric inconsistency
Patsnap Eureka Solution