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Virtual Engineering: Designing Hardware in Simulation

Using high-fidelity simulation to design and validate perception hardware before silicon exists - extending synthetic data to hardware design.

Evyatar Bluzer
3 min read

A depth sensor decision that would have taken 6 months with physical prototypes took 3 weeks in simulation. Synthetic data already trains our algorithms; virtual engineering points the same machinery at designing the hardware itself.

The Concept

Traditional hardware development runs in a loop nobody enjoys: design sensor specs, build prototypes, test in the lab, discover issues, redesign, repeat. Each pass through the redesign step is expensive and slow.

Virtual engineering reorders the loop. Build a high-fidelity sensor simulation, test algorithm performance on the simulated sensor, iterate the sensor design in simulation, and only build a prototype once you are confident. Physical validation then confirms predictions instead of generating surprises.

Virtual engineering loopsFive boxes in two rows connected by arrows: design spec, sensor simulation and algorithm evaluation across the top with a highlighted loop arrow back to the design spec; prototype and physical validation below, with an arrow feeding measured error back up into the simulation. Sensor design spec ToF 320x240 or stereo 640x480 Sensor simulation optics, sensor noise, timing, scene and lighting Algorithm on simulated output hand tracking: accuracy, power, latency iterate the design in simulation: hours per pass Prototype built once, when confident Physical validation golden reference sensor, real captures decision after 3 weeks, not 6 months of prototypes prediction error fed back correlation 0.92 depth noise, 0.85 feature detection
The two loops of virtual engineering: design iterations run through the simulator in hours, the prototype is built once, and its measured error feeds back into the sensor models, which currently track reality at 0.92 for depth noise and 0.85 for feature detection.

What We Simulate

Optical system: ray tracing through the lens elements, covering distortion, vignetting, chromatic aberration, depth of field effects, stray light, and ghosting.

Sensor model: quantum efficiency vs wavelength, dark current and read noise, fixed pattern noise, pixel crosstalk.

System integration: timing between sensors, data path latencies, synchronization errors, thermal effects.

Scene interaction: realistic environments, lighting conditions, material properties, dynamic elements.

Case Study: ToF or Stereo?

The question on the table: should the next device use ToF at 320x240 or stereo at 640x480? We built accurate models of both sensor options, rendered diverse scenes through each, ran the hand tracking algorithm on the simulated output, and measured accuracy, power, and latency for both.

The answer was not clean, which is exactly why the exercise was worth doing. ToF wins in low light, stereo wins in sunlight. We chose ToF with a stereo fallback. With physical prototypes this decision would have taken 6 months. In simulation it took 3 weeks. The last depth sensor bake-off I ran had four vendor kits sitting in a lab for months; that is the loop this replaces.

The Validation Problem

A simulation that does not predict reality is a very fast way to make wrong decisions. So we validate against hardware we trust: build a golden reference sensor with known characteristics, capture real data, compare simulated and real sensor outputs, measure the prediction error, and feed the error back into the simulation models. Current simulation-to-reality correlation sits at 0.92 for depth noise and 0.85 for feature detection.

Organizational Impact

The tooling changes how teams work together. Hardware and algorithm teams share a common tool, design decisions come with quantified impact, iteration happens in hours instead of months, and risk gets retired before the expensive commitments are made. During lockdown I claimed that a simulated sensor design change could be evaluated in hours instead of months. An iteration does run in hours. A decision you would commit a device to took three weeks, which is the honest version of that claim.

Scaling the Capability

For this to spread beyond a specialist team it has to be self-service: simulation tools that do not require an expert, pre-built sensor models for common components, standardized evaluation metrics, and integration with existing hardware design workflows. The goal is every hardware decision backed by simulation data.

The Limits

Virtual engineering does not replace physical testing. Manufacturing variation, long-term reliability, user experience factors, and unexpected real-world conditions all still need real hardware in real hands. But cutting physical iterations from 10 down to 2 changes the pace of development completely, and that is the whole point.

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