For years, artificial intelligence has advanced by chasing scale: more data, more compute, bigger models. It worked—until it didn’t.
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At EvoChip, we believe the next chapter of AI won’t be defined only by what’s possible in a hyperscale data center, but by what’s sustainable everywhere else. In homes, hospitals, factories, and vehicles — where power is scarce, silicon is constrained, and intelligence must operate at the edge.
This is where today’s AI infrastructure begins to crack. And it’s where EvoChip has chosen to begin.
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The Edge Is demanding more than we can give
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From smart sensors in manufacturing plants to autonomous drones and medical implants, edge devices are being asked to do more — to run real-time models, learn on-device, and operate autonomously, often without connectivity. But they run on milliwatts. They lack cooling. They use limited silicon. The standard AI stack — built for GPUs and matrix math — was never meant to live in this world. It draws too much power, needs too much memory, and scales poorly when power or bandwidth drop out.
At EvoChip, we didn’t just optimize the existing stack. We replaced it.

The EvoChip solution: One architecture, all scales
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Our patented technology is not a variant of neural networks — it’s a departure. We use evolutionary learning algorithms and binary logic structures to build models that are ultra-efficient, modular, and compact. No floating-point math. No matrix multiplications. No dependence on GPUs. This allows us to train and deploy AI directly on hardware — including low-power edge platforms, software-defined systems, and custom ASICs.

In real-world tests, our architecture has achieved up to 1,000x efficiency gains, including a demonstration where we processed over 1 billion inferences per second (IPS) on a 9-volt-powered 200$ FPGA board.

But critically, this isn’t just about small devices. The same architecture scales.
From tiny edge devices, to specialized small and mid-sized models (SMLs), and tomorrow to large language model (LLM) training tasks, EvoChip logic adapts — flexibly, efficiently, and without the bloated overhead of traditional AI pipelines. One core architecture, spanning from embedded intelligence to state-of-the-art compute.

Not “Lite AI” — Smarter AI

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There’s a myth that edge AI, due to limited resources, must always be a scaled-down version of centralized models. But the real opportunity is to rethink how intelligence is designed — not just how it’s shrunk. EvoChip delivers models that are not only smaller and faster, but more robust and generalizable. Our learning engine avoids overfitting, adapts faster to new data, and performs reliably across unpredictable environments — whether it’s embedded in a smart sensor or training across large amount of data.
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In a world where intelligence needs to be everywhere, flexibility isn’t a feature — it’s a requirement.

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From battery to cluster — With one stack

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Our technology is delivered as modular IP. It can be deployed today on commercially available FPGAs, ported to full-custom ASICs, or integrated into mixed-scale software and hardware pipelines. Engineers can prototype in software and move to silicon without redesigning the model.
That means startups building smart devices, OEMs embedding AI into edge hardware, and cloud providers can all benefit from the same core logic — purpose-built for performance per watt, per transistor, per dollar.

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The Future of AI Is hybrid, distributed, and efficient

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AI is no longer confined to labs or data centers. It’s entering the field — embedded in things we carry, drive, wear, and rely on. To meet that moment, we need more than faster chips. We need fundamentally more efficient architectures that scale without compromise.
EvoChip offers that architecture — lean enough for the edge, powerful enough for the cloud, and flexible enough to grow across the full spectrum of AI workloads.

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The edge can’t wait. Neither can the future. With EvoChip, it no longer must.

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