Energy Saved
29.6x less energy on real GPU workloads with 35x less data movement.
MeasuredBenchmarks
Numbers are useful only when the context is visible.
EnergyIR separates measured results from projections and keeps workload, baseline, energy, movement, speedup and caveats attached to each claim.
Proof in action
Measured today. Projected tomorrow.
All figures have provenance. Nothing is asserted from marketing.
Speedup
Up to 8.80x measured speedup on real GPU matrix workloads.
MeasuredPB64 Efficiency
Up to 51x lower energy per stochastic update.
ProjectedMatrix Operations
Projected energy advantage for matrix-vector multiply with resident weights.
ProjectedLinear Solves
Projected advantage for linear solves vs digital Od3 methods.
ProjectedBenchmark notes
Claims shown with workload, baseline and caveat context.
The public page keeps the language clear while leaving room for deeper partner documentation under NDA.
WorkloadBaselineStatusEnergyMovementSpeedup / caveat
GPU matrix workloadsCommercial GPU baselineMeasured157.9 J saved35x less movementUp to 8.80x
Stochastic updateDigital stochastic computeProjectedUp to 51x lower energyResident statePB64 efficiency
Matrix-vector multiplyDigital resident-weight pathProjected1.86-36x advantageWeights remain localWorkload dependent
Linear solvesDigital Od3 methodsProjected6-1506x advantageReduced transfer pressureProblem-size dependent
6U energy scenario
More mission time from the same power system.
A typical 6U Earth-observation satellite may operate with a 15 W onboard AI processor, a 99 Wh battery, and solar generation of approximately 19.2 W from body-mounted panels or 38.4 W from a deployable array.
In that environment, computation is not an abstract cost. Every watt assigned to processing is a watt unavailable to the payload, communications system, attitude control, thermal management or battery recharge.
EnergyIR is designed to increase the amount of useful computation available inside that fixed mission-energy envelope.
Compute configurationDemandBattery durationShare of 19.2 WShare of 38.4 W
Conventional AI processor15.00 W6.6 hours78.1%39.1%
EnergyIR measured residency case11.89 W8.3 hours61.9%31.0%
EnergyIR matrix-compute floor10.15 W9.8 hours52.8%26.4%
EnergyIR projected resident-decode case5.12 W19.3 hours26.7%13.3%
Mission meaning
Energy efficiency becomes mission capability.
A reduction in compute power does more than extend battery duration. It can translate directly into more sensing, more intelligence and more autonomous operation.
Measured system efficiency
1.7 additional hours of computeCompute demand falls from 15 W to approximately 11.9 W, releasing around 3.1 W for other spacecraft systems and avoiding approximately 74.5 Wh over 24 hours of continuous processing.
Matrix-processing scenario
6.6 hours to 9.8 hoursAt the current periphery-inclusive matrix-compute floor, the same battery supports more than three additional hours of processing and approximately 116.5 Wh less energy use per day.
Resident AI inference
6.6 hours to 19.3 hoursFor memory-bound batch-one AI inference using resident weights, the projected architecture could reduce compute demand to approximately 5.1 W in this illustrative scenario.
Less heat generated inside the spacecraftReduced thermal-management pressureLonger payload duty cyclesMore processing during eclipseMore useful information extracted before downlinkSmaller power-system requirements for a given missionGreater resilience when generation or battery capacity degrades
Operating model
Process more. Transmit less. Operate longer.
EnergyIR combines energy-aware software, workload residency, specialised computation and verifiable execution to help spacecraft turn fixed energy into mission capability.
Process data onboard
Avoid unnecessary memory and communication movement
Run more inference within the same power envelope
Reserve scarce energy for mission-critical systems
Produce a signed record of what was computed and why the result can be trusted
It is not simply to make a processor consume fewer watts. It is to convert a fixed spacecraft energy budget into more sensing, more intelligence and more autonomous operation.
More intelligence in every spacecraft watt.Evidence note
Measured, simulated and projected cases are separated.
The scenario isolates compute contribution only. It is useful for mission planning discussion, not a substitute for full spacecraft power analysis.
This is an illustrative 6U mission scenario intended to isolate the compute contribution to the spacecraft power budget.
The 11.89 W residency case is derived from EnergyIR's measured 1.42x system-energy improvement on existing GPU hardware.
The 10.15 W matrix-compute case is based on a simulated, periphery-inclusive 1.86x hardware floor.
The 5.12 W resident-decode case is a design projection for a memory-bound workload and remains subject to fabrication, radiation hardening, qualification and inclusive flight-hardware measurement.
Actual spacecraft endurance will also depend on payload sensors, communications, attitude control, thermal systems, power-conversion losses, eclipse duration and battery operating limits.
Mission fit
Request benchmark notes for your workload.
EnergyIR can map your workload against the right baseline and identify what can be measured now versus what remains projected.
Trust signals
Built for mission environmentsDesigned for radiation-aware computeBenchmark-backed architectureExport-control aware collaboration