NISYS GmbH · Nature-inspired computing

Ekaansh AI systems that mimic nature's power and efficiency

Unlike today's neural networks, oscillatory networks harness the way biological systems compute. By leveraging physics instead of fighting it, the systems we build achieve the energy efficiency and intelligence density that nature demonstrates.

Explore the approach Join the team

The next revolution in computing will be inspired by nature. Come along.

Mission

AI for the planet

We build radically new forms of AI that serve both humanity and the environment. Intelligence should not be a black box controlled by a few. It should be a transparent, accessible tool for the many.

  • Transparent Systems whose behaviour can be understood, inspected and trusted.
  • Accessible Intelligence that runs where it is needed, without a data centre behind it.
  • Measurable Impact you can count: for individuals, for industry, and for a more equitable technological future.

01 The problem

Today's AI is burning through nature and money

AI will unlock unprecedented progress, but current hardware cannot sustain the pace. Classical digital chips are hitting a wall: soaring energy costs, skyrocketing water consumption for cooling, and a fundamental thermodynamic dead end.

Data in Bits are moved from memory to logic, over and over.
Result out A small fraction of the energy becomes the answer.
Everything else Dumped as waste heat, then removed with water and more power.
Every computation on a conventional chip takes in data, produces a result, and dumps everything else as heat. In this paradigm, more intelligence means more heat, more cost, and more environmental damage.
Energy

Training and serving large models draws grid-scale power, and demand grows faster than efficiency gains.

Water

Cooling the heat that digital logic produces consumes fresh water at a scale that competes with people and agriculture.

Thermodynamics

Shrinking transistors no longer buys efficiency. The digital paradigm itself has reached its physical limit.

02 Our approach

Leverage physics instead of fighting it

Biological brains do not shuttle bits between memory and logic. They compute with rhythms. Neurons are coupled oscillators, and information lives in their phases and frequencies. Computation emerges from the dynamics themselves.

Oscillatory neural networks bring that principle to silicon. Instead of forcing an analog world to behave digitally, the hardware lets physical oscillators interact and settle into a solution. Synchronisation does the work that today's chips do with billions of multiply-accumulate operations, at a fraction of the energy.

Live model · Kuramoto oscillators

Watch a network compute by synchronising

Each dot is an oscillator with its own natural frequency. Turn up the coupling and they lock into a shared rhythm. That collective state, not a stream of instructions, is the computation.

Coherence r 0.00

dθi/dt = ωi + (K/N) Σj sin(θj − θi)

03 What we build

From learning rules to silicon

We work across the full stack of a new computing paradigm, so that theory, algorithms and hardware are designed together rather than bolted on afterwards.

  1. Network architectures & learning

    Novel recurrent architectures and learning rules grounded in biological principles, analysed for their dynamics and trained to solve real tasks.

  2. Neuromorphic hardware

    Brain-inspired chip architectures, from FPGA prototypes to analog mixed-signal designs that let oscillators do the computing.

  3. Systems with measurable impact

    Deployable intelligence that delivers for individuals and industry while cutting the energy and water cost of AI.

Join us

Shape the future of nature-inspired intelligent computing

We are building computing systems inspired by nature's most efficient designs, from oscillatory neural networks to bio-inspired chip architectures. Join a team of world-class researchers, engineers and visionaries tackling the fundamental challenges that will define how computation evolves.

  • Senior Research Scientist

    Apply

    Network architecture design · Theoretical analysis · Dynamics & learning algorithms

    You will design, train and analyse novel recurrent network architectures and learning rules inspired by biological principles.

  • Lead Hardware Engineer, FPGA and Analog

    Apply

    Neuromorphic hardware · FPGA & mixed-signal design · Technical leadership

    As Lead Hardware Engineer, you will spearhead the development of brain-inspired AI hardware, from FPGA prototyping and analog mixed-signal concepts to building and guiding the hardware team.

Speculative applications

We are always eager to connect with exceptional talent. Whether you are a scientist, an engineer, an artist or a tinkerer who shares our vision, we want to hear from you. Reach out and let us explore how we can shape the future of computing together.

Get in touch

Contact

Let's talk about the next computing paradigm

For research collaborations, partnerships, press, or a conversation about joining the team.

hello@nisys.ai