Welcome to Tipalo


Who we are

Tipalo GmbH is a Swiss LLC, while the name is an abbreviation for Time based pattern logic.
We are a start up IT company, with own hardware + software, to pioneer logic applications.
We develop a new IT technology for machine based logic using an own self-learning mechanism.

The complexity of our neural net based software is the equivalent of an Artificial Nervous System.


What we do

Biologically inspired general-purpose AI -->

a digital brain with an Artificial Nervous System


How we do

The brain hardware consists of a board with a high capacity FPGA,
with interfaces to connect with different sensors and actors
of a certain given body hardware, e.g. a framework with limbs and head.
This brain hardware within the body hardware is an embedded system.

The brain software consists of the following components:
a. the operating system, implemented in VHDL as the FPGA bitstream
b. different applications connected with each other as an ANS, means
they build together the equivalent of an Artificial Nervous System.

The applications are implemented only as self-programmable neural nets,
while these neural nets are connected with each other within the applications
via the also self-programmable networks on chip, where all applications
are also connected with each other via self-programmable networks on chip.

These self-programmable networks are connected to the body hardware
with the sensors and actors via the board Ethernet interfaces.
The corresponding drivers needed to connect the sensors and actors
are also implemented in dedicated self-programmable neural nets.

This ANS will be processed in real-time by the operating system,
while enabling reaction times similar to a biological neuron,
this implies that all active neurons at a given time are processed
within one milisecond, permanently 1000 times in a second, 24/7 forever.

The ANS has a remarkable feature called SLM, Self Learning Mechanism,
enabling the development of the brain to learn and accumulate knowledge,
only by reacting autonomously with the surrounding environment with its body.
This feature is the result of the interaction in time between different neural nets.