Brain-Inspired AI for the Future of Intelligence
We are revolutionizing artificial intelligence by developing foundational models that mirror the efficiency of the human brain, delivering data-center performance on a battery.
The Problem: AI is Hitting a Wall.
Modern AI faces an unsustainable energy crisis. The most powerful models are trapped in the cloud, consuming megawatts of power and creating a hard "power wall" that prevents high-performance intelligence from running on the devices that need it most.
Unsustainable Energy Costs
Training large AI models consumes massive amounts of energy, leading to high operational costs and environmental concerns, limiting their accessibility and scalability.
The Edge AI Bottleneck
Companies are forced to deploy smaller, less capable models on edge devices, compromising the safety, functionality, and competitiveness of products in robotics, automotive, and IoT.
Performance vs. Power Trade-off
The industry is stuck in a trade-off: high performance requires high power. This fundamental conflict holds back the entire multi-hundred-billion-dollar edge AI market.
The Solution: Aneuro
Aneuro is a new class of foundational AI model based on a novel, brain-inspired Spiking Transformer architecture. It is not an incremental improvement; it is a paradigm shift in computation that eliminates the trade-off between performance and power.
The Proof: Performance per Watt
Our 300 and 800 million-parameter Aneuro models already outperform traditional models up to 10 times their size on industry benchmarks. Explore the data below to see how Aneuro compares. Use the slider to filter models by size.
Core Technological Innovations
Aneuro's breakthrough performance is rooted in a series of deeply integrated, bio-plausible innovations that fundamentally change how AI computes information (Patent Pending).
🧠 Multiplication-Free Spiking Self-Attention
+We replaced the power-hungry matrix multiplications in traditional Transformers with ultra-efficient, sparse computations. By encoding information as binary spikes, our attention mechanism runs on simple logical operations and additions, drastically reducing energy consumption.
🕰️ Comprehensive Spatial-Temporal Attention
+Unlike other models, Aneuro processes information in both space and time, mimicking the brain's ability to understand dynamic context. This allows for a richer understanding of the world while maintaining computational efficiency.
🧬 Brain-Like Learning (STDP)
+Our training methodology is inspired by Spike-Timing-Dependent Plasticity, a fundamental mechanism of learning and memory in the brain. This allows Aneuro to learn more efficiently and form robust, adaptive representations from data.
⚙️ Hardware-Ready Architecture
+The entire model is a true "spike-driven" system, designed from the ground up to be compatible with the next generation of low-power neuromorphic hardware, unlocking its full energy-saving potential.
The Vision: The Industry Standard for Efficient AI
By licensing the Aneuro foundational model, we will enable a new generation of intelligent devices and unlock the full potential of the edge AI market. This is the first, humble step on a longer journey: to build a true, brain-like intelligence and fundamentally change the future of computing.
Unmatched
Efficiency
Leveraging event-driven computation, our SNN models process information with minimal redundancy, enabling faster learning and real-time adaptability while maintaining high accuracy.
Low
Computational Power
Inspired by biological neurons, and their information encoding, our models drastically reduce energy consumption compared to traditional deep learning, making AI more scalable and sustainable.
A Smarter Path to
General AI
By mimicking the way the human brain learns and adapts, our approach moves beyond static models, paving the way for more flexible, generalizable, and intelligent AI systems.
Building the Future of Brain-Inspired Intelligence
We are revolutionizing AI by developing spiking neural network foundational models that mirror the efficiency and adaptability of the human brain. Our goal is to create intelligent systems that require less power, learn dynamically, and bring us closer to true general AI.