STAGE 01 OF 05
Workload Intelligence
The AI Workload Fingerprint™ reads what a model actually needs from silicon — before a single design decision gets made.
WHAT IT DOES
Three things this stage produces
Workload Fingerprinting
Profile compute, memory, and I/O patterns from real inference traces — not assumptions about what your model probably does.
Bottleneck Mapping
See exactly where a general-purpose chip wastes power or cycles running your specific workload.
Requirements Brief
A concrete spec — throughput, latency, power envelope — that feeds straight into Virtual Silicon, stage two.
INPUTS AND OUTPUTS
What this stage needs, and what it hands on
WHAT GOES IN
Trained models, in formats such as ONNX, TensorFlow, or PyTorch
Real inference traces from your target use case
Product constraints: power budget, latency, size, cost
WHAT COMES OUT
Workload fingerprint
Bottleneck map on current hardware
Requirements brief for Virtual Silicon
CLEAN FUTURE CF-1
Workload Intelligence on CF-1
We profiled the edge vision and sensor-fusion models CF-1 must run, and set its performance-per-watt target from that fingerprint rather than from a generic benchmark.
Every architecture decision downstream starts here.
Tell us about your workload and we'll tell you what it needs from silicon.
Talk to us
— From AI to Silicon
© [Year] PathinAI Technologies Private Limited