Inference at the boundary.
A lightweight inference runtime built for edge hardware — ARM, RISC-V, and custom NPUs. Real-time machine learning at the device layer, with no cloud dependency and no compromise on capability.
Active research programme · Pre-alpha · Waitlist open
"Field agent; one who sees from afar and acts promptly."
The name reflects the core intent of the project — bringing perceptive intelligence to devices at the edge of a network, far from the cloud, operating in real time on constrained hardware with limited or no connectivity.
Where SEER gives visibility into AI systems that run in the cloud, Kàkàņfò brings intelligence to the hardware that sits at the physical boundary of the world — sensors, robots, medical devices, industrial equipment, and the billions of edge nodes that cloud-native ML cannot reach.
Most machine learning assumes a network connection, a GPU server, and milliseconds to spare. For the majority of the world's hardware — industrial sensors, medical implants, mobile robots, satellite uplinks — none of those assumptions hold.
Kàkàņfò is a software layer that sits between your trained ML model and the hardware it runs on. It handles the hard parts — memory layout, kernel scheduling, power management, quantisation — so the model runs efficiently regardless of what chip it lands on.
Kàkàņfò compiles model operations directly to the instruction set of the target hardware — NEON for ARM, vector extensions for RISC-V, custom kernels for NPUs. No generic fallbacks. Runs as fast as the hardware allows.
Models are quantised to INT8, INT4, or binary representations at runtime, with automatic calibration to preserve accuracy. A model that runs at 94% accuracy in FP32 typically runs at 91–93% at INT8 — with 8x less memory and 4x the speed.
Kàkàņfò monitors available power budget in real time and adjusts inference precision and batch behaviour dynamically. A device on battery power infers at INT4. When plugged in, it upgrades to INT8 automatically — no code changes needed.
Models are deployed to devices as compact binary packages over any transport — BLE, LoRa, USB, or direct flash write. Kàkàņfò handles versioning, rollback, and A/B deployment across fleets of devices, even in disconnected environments.
Import from TensorFlow Lite, ONNX, PyTorch Mobile, or CoreML. Kàkàņfò handles the conversion and optimisation internally. You train in whatever environment you prefer — Kàkàņfò handles the edge deployment.
Any environment where intelligence is needed but cloud connectivity cannot be assumed. Kàkàņfò is designed for hardware that operates at the boundary — physically, economically, or logistically — of the connected world.
Predictive maintenance models running directly on vibration sensors and PLCs. Detect bearing failure, anomalous heat signatures, and process deviations in real time, at the machine, without sending data off-site.
Perception and decision models running on the robot's own compute — no latency penalty from cloud round-trips. Object detection, path planning, and anomaly avoidance at the speed the hardware demands.
Classification and signal analysis on implantables, wearables, and point-of-care devices. Runs within strict power budgets and memory constraints, with the reliability standards medical hardware requires.
Agricultural sensors, environmental monitors, and utility infrastructure in areas with no reliable connectivity. Inference runs locally, data is summarised on-device, and syncs when connectivity is briefly available.
In-vehicle classification and sensor fusion running on automotive-grade ARM MCUs. From ADAS assistance features to fleet telematics — processed locally, with no dependency on mobile network availability.
On-device intelligence for smart home, wearable, and consumer electronics manufacturers who want to add ML capability without the cost, latency, or privacy exposure of cloud inference.
Kàkàņfò is an active research programme inside Cadence Labs' Edge & Distributed Intelligence domain. We are currently in the pre-alpha phase — the core runtime is functional on ARM Cortex-A hardware, and RISC-V support is in development.
We are building the waitlist to understand the landscape of use cases and hardware targets before we move into closed alpha. Waitlist members directly influence our roadmap — we read every response and reach out to learn more.
If you're working on a problem that needs on-device ML, we want to hear about it now — not after we launch.
Join the waitlistWe're opening a small, selective waitlist for teams who are working on edge ML problems right now. Waitlist members get early access to the pre-alpha SDK, a direct line to the research team, and the ability to shape what we build next.
We are not collecting emails to send a launch newsletter. We are looking for partners who have a real problem and real hardware. If that's you, tell us about it.
Hands-on access to the Kàkàņfò runtime before public release, with direct support from the engineering team.
Your use case and hardware targets directly affect what we build next. We will reach out and ask questions.
If your problem is novel enough, we may invite you into a formal research collaboration with the Cadence Labs team.
Waitlist members lock in founder pricing when Kàkàņfò launches commercially. That price will not be available after the public beta.
We read every submission. You will hear from us within 5 business days.