Neuromorphic computing
Neuromorphic computing for AI that runs on watts, not racks.
Neuromorphic computing is brain-inspired hardware that keeps memory and compute together and runs on events instead of a clock, so it draws power only when the input changes. We build and benchmark it for perception on drones, sensors, wearables and satellites, where the power budget decides what can be fielded at all.
What neuromorphic computing is
Conventional processors separate memory from compute and march to a clock, so much of their energy goes on moving data rather than doing arithmetic. Neuromorphic processors put the two together and compute with spiking neural networks: a neuron only fires, and only costs power, when it has something to report.
That makes the hardware a natural fit for always-on sensing, where most of the time nothing interesting is happening. A camera watching an empty corridor, a vibration sensor on a healthy pump, a microphone in a quiet room: a clocked accelerator works just as hard on those as on the moment that matters, while an event-driven chip mostly waits.
What we measured: 9 W against 120 W
In our lab in San Antonio, a hybrid deployment pairing a BrainChip Akida neuromorphic processor with an NVIDIA Jetson held the same accuracy at 9 watts that the Jetson alone needed 120 watts to reach. The Akida handles the always-on, event-driven front end; the Jetson wakes for the heavier work only when something has happened.
That difference is the whole argument. A system that needs 120 W needs a vehicle, a generator or a mains supply. A system that needs 9 W can live on a battery, a small solar panel or the power budget of a drone. The full write-up, with the setup and its limits, is on our blog.
BrainChip Akida and NVIDIA Jetson are partners, not rivals
Akida is a commercial neuromorphic processor from BrainChip, programmed through its MetaTF tooling. Jetson is NVIDIA's embedded GPU platform, programmed through JetPack, CUDA and TensorRT. They are good at different things, and the useful design question is how to split a workload between them, not which one wins.
Neuromorphic front end
Keyword spotting, anomaly detection, presence and change detection, simple classification on sensor streams: always on, low duty cycle, tiny power draw.
GPU when it matters
Detailed vision, larger models, sensor fusion and anything that needs the mature GPU software stack, woken by the front end rather than running continuously.
One power budget
The split is set by the platform's watts, the latency target and the accuracy floor, measured on the real hardware rather than taken from a datasheet.
The honest limits
Neuromorphic hardware is not a GPU replacement. Training large models stays on conventional accelerators, the toolchains are younger than PyTorch, and converting a model to spiking form is real engineering. The teams that benefit first pick the right workload, usually perception, anomaly detection or always-on sensing, rather than trying to move everything.
How an engagement works
We start with one workload and one platform. We measure the current baseline at the wall, identify the always-on portion that suits event-driven hardware, build and test the hybrid design in our lab, and hand over the measured power, latency and accuracy along with the code. If neuromorphic is the wrong answer for the workload, the measurement says so and so do we.
Questions about neuromorphic computing
What is neuromorphic computing?
Neuromorphic computing is brain-inspired hardware that keeps memory and compute together and runs spiking neural networks on events rather than a clock. Because a neuron only fires when its input changes, the chip draws power mainly when something happens, which suits always-on sensing at the edge.
Is BrainChip Akida better than NVIDIA Jetson?
They do different jobs. Akida is efficient at always-on, event-driven tasks such as detection and simple classification; Jetson runs larger models and the mature GPU software stack. In our lab a hybrid of the two held the same accuracy at 9 W that the Jetson alone needed 120 W for.
Can neuromorphic chips train large models?
No. Training large models stays on GPUs and other conventional accelerators. Neuromorphic hardware is for running suitable models efficiently at the edge, not for training them.
Which workloads suit neuromorphic hardware?
Workloads that watch continuously but act rarely: keyword spotting, anomaly and change detection, presence detection, vibration and acoustic monitoring, and other sensor-stream classification on platforms with a tight power budget.
Do you work on neuromorphic projects for defense and government?
Yes. The AI Cowboys is a Service-Disabled Veteran-Owned Small Business in San Antonio, registered in SAM.gov, and our edge and neuromorphic work is designed to run on the platform or inside a customer's perimeter with no link to an outside cloud.
Go deeper
- Our neuromorphic chip on NVIDIA Jetson
The lab write-up behind the 9 W figure.
- BrainChip Akida vs NVIDIA Jetson
Where each fits, and how to split a workload.
- Why neuromorphic computing matters
The strategic case, and its limits.
- Spiking neural networks at the edge
Event-driven sensing for real-time protection.
- Edge, on-prem and air-gapped AI
Deploying the rest of the stack offline.
- Research and publications
Papers with permanent DOIs.
Start with a briefing: one real problem, the constraints around it, and what a scoped pilot would measure.
Talk to The AI Cowboys