CHAPTER ONE / ONBOARD UAV PERCEPTION
Every machine that moves has to perceive first. Rudr AI builds the fastest, lowest-power perception stack for autonomous machines: event-driven sensing and neuromorphic compute, running on a battery, reacting in microseconds to milliseconds.
WHAT WE BUILD
Rudr AI is a hardware and software company. The module combines four parts that are built to reinforce each other, so sparsity in the sensor is never undone by the compute that follows.
A sensor whose pixels fire asynchronously, only when the scene changes. Output tracks motion, not a clock.
Processors that sit idle until an event arrives, so compute happens only where something changed.
Proprietary tooling that turns already-trained networks into sparse, event-native form without retraining from scratch.
Models developed in-house that read raw event streams directly, rather than reconstructing dense frames first.
THE PROBLEM
A frame camera ships every pixel at a fixed rhythm whether or not anything changed. The processor grinds through all of it to find the small part that matters, fetching data across the memory boundary each time. That is fine on wall power. It is wrong for a battery with milliseconds to react.
THE SPARSE STACK
Two halves that close the loop dense pipelines leave open. Neither one forces the other back into a dense representation along the way.
SENSOR
Each pixel is independent and asynchronous. It emits only when its log-intensity crosses a threshold: a compact tuple of where, when, and brighter or darker. The output data rate tracks motion, not a clock.
COMPUTE
A neuron transmits only when its activation changes past a threshold, carrying the graded magnitude. Convert an already-trained network into sigma-delta form for temporal and spatial sparsity, with no from-scratch spike training.
WHY THEY BELONG TOGETHER
You are not paying for idle scenes. Average power tracks how much is actually moving.
No frame boundary to wait for. A change is processed within microseconds of occurring.
Ship a trickle of coordinates and polarities instead of a full video stream.
WHERE IT DEPLOYS
One perception module. It begins on the machines with the least room to spare, and the same architecture extends outward from there.
OUR BET
A large part of it will happen in the physical world, in machines that move, gated not by how large a model can be but by how efficiently a machine can perceive and act under real constraints. If the signal is sparse, the sensor and the compute should both be sparse.