K-Scope: Visual Profiler
K-Scope
The MRI scanner for your AI application. Visualize latency, memory, and power consumption with microsecond precision.
Supported Devices
The Observability Gap
Traditional edge AI debugging involves reading text logs that output "Total Inference Time: 45ms". But this single number hides the truth.
Why is it 45ms? Is it the convolution layers? Is the USB bus choked? Is the host CPU too slow to feed the NPU?
The Visual Insight
K-Scope instruments the firmware to emit lightweight trace events. It visualizes these events on a unified timeline, similar to NVIDIA Nsight Systems.
- Unified Timeline: See Host CPU, USB Bus, and NPU Compute on one axis.
- Concurrency Check: Verify that your data transfer overlaps with compute (Double Buffering).
- Power Profiling: Correlate high-power spikes with specific model layers.
Targeted Optimizations
Solve specific hardware challenges.
Battery Optimization
KL520 Focus
For battery-operated smart locks, "Always-on" means milliwatts matter.
K-Scope visualizes power draw over time. Identify if your model keeps the NPU awake longer than necessary or if unoptimized layers are causing power spikes.
Bandwidth Analysis
KL530 Focus
Transformers (ViT) are memory hungry.
The DRAM Heatmap shows memory bandwidth saturation. Identify layers that are "Memory Bound" vs "Compute Bound" and optimize channel dimensions accordingly.
System Latency
All Devices
Latency is more than just inference.
Visualize the full "Glass-to-Glass" latency: Camera Capture -> Pre-processing -> USB Transfer -> NPU Inference -> Post-processing. Find the weakest link in the chain.
Automated Bottleneck Detection
You don't need to be a hardware expert to use K-Scope. The "Insight Engine" automatically analyzes your trace and flags common issues.
- USB Bottleneck DetectedThe NPU is idle for 40% of the time waiting for input data. Consider increasing batch size or using async USB transfer.
- Thermal Throttling RiskSustained peak power exceeds 2W for >500ms. Consider reducing clock speed or optimizing 'Conv_4' layer.
Audit a ResNet50 Trace
We've captured a trace of an unoptimized model. Can you find the memory bottleneck?
Launch K-Scope Mockup