The case for neuromorphic and stochastic computing is efficiency — so it should be measured, not asserted. SC-NeuroCore's compiler estimates the embodied carbon of making a part and the operational carbon of running it for five years, across targets and grid regions, and checks the thermal envelope — producing the kind of carbon artefact EU labelling will expect from 2027.
A device's footprint is the carbon spent making it plus the carbon spent running it. Manufacturing (embodied) carbon is fixed by the fabrication process; operational carbon is the energy it draws over its lifetime multiplied by how clean the local grid is. The second term is where deployment choices — and neuromorphic efficiency — pay off.
Operational only; embodied manufacturing carbon adds a fixed per-target amount on top. Grid intensities are representative annual figures and are adjustable in the tool; the point is the ranking, not a certified number.
The compiler exposes a single call that returns both carbons for a target at a given power: the embodied manufacturing carbon and the total over a five-year lifetime. Comparing targets — an FPGA, a Loihi-class part, a micro-controller, a research substrate — becomes a one-line sweep, so the sustainability trade-off is on the table at design time, not discovered after deployment.
The same design emits an order of magnitude more carbon on a coal-heavy grid than on a hydro or nuclear one — the region term $G$ swings from roughly $0.02$ to $0.8$ kg CO₂/kWh. Making that visible lets a team pick the greenest viable deployment, and lets the efficiency of the hardware and the cleanliness of the grid be weighed together.
Energy that goes in comes out as heat, and a part that overheats is a part that fails or throttles. The toolkit checks the junction-temperature envelope — power, thermal resistance and ambient into a predicted die temperature with derating — so a design that is efficient on paper is also thermally deployable in its enclosure.
Carbon and thermal estimates are not a report bolted on at the end — they are inputs the compiler can act on, alongside power-intent (UPF) and energy schedules. The same intelligence that lowers a model to hardware can weigh a target and a configuration by their energy and carbon, making sustainability a compile-time objective rather than a post-hoc audit.
Regulation is arriving: EU carbon labelling for electronics is slated to become mandatory from 2027. A design that already emits an embodied-plus-operational carbon estimate, with its grid and lifetime assumptions stated, has the artefact that regime will ask for — built during design rather than reconstructed under deadline.
These are model-based estimates, not a certified life-cycle assessment. Grid intensities are representative regional figures and are adjustable; embodied carbon is an estimate for a fabrication class; the five-year window and duty cycle are assumptions you set. What the tool gives you is a consistent, comparable, design-time carbon number with its inputs on the table — enough to rank options and steer a decision, and to seed a proper LCA rather than replace one.