  Highly granular pixel detectors allow for increasingly precise measurements
of charged particle tracks. Next-generation detectors require that pixel sizes
will be further reduced, leading to unprecedented data rates exceeding those
foreseen at the High Luminosity Large Hadron Collider. Signal processing that
handles data incoming at a rate of O(40MHz) and intelligently reduces the data
within the pixelated region of the detector at rate will enhance physics
performance at high luminosity and enable physics analyses that are not
currently possible. Using the shape of charge clusters deposited in an array of
small pixels, the physical properties of the traversing particle can be
extracted with locally customized neural networks. In this first demonstration,
we present a neural network that can be embedded into the on-sensor readout and
filter out hits from low momentum tracks, reducing the detector's data volume
by 54.4-75.4%. The network is designed and simulated as a custom readout
integrated circuit with 28 nm CMOS technology and is expected to operate at
less than 300 $\mu W$ with an area of less than 0.2 mm$^2$. The temporal
development of charge clusters is investigated to demonstrate possible future
performance gains, and there is also a discussion of future algorithmic and
technological improvements that could enhance efficiency, data reduction, and
power per area.
