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Mission

Intelligence
Unplugged.

Data-Center Performance on a Battery. We build Brain-Inspired Spiking Foundational Models that run locally, efficiently, and privately.

The Energy Wall

Modern AI is hitting a wall. Power-hungry Transformers are trapped in the cloud, consuming massive energy and causing the Edge Bottleneck.

Dense vs Spiking

Energy Crisis

Unsustainable power draw makes global scaling impossible and environmentally taxing.

The Edge Gap

Disconnected devices remain "dim" - unable to process complex vision without cloud access.

Brittle Links

Forced dependence on 5G/Fiber creates critical failure points in autonomous missions.

Feature Traditional AI Aneuro (Spiking)
Visual Dense Grid
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Heavy Math: Every layer performs billions of float multiplications per second, regardless of data content.
 Sparse Spark
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Pure Signal: Only active spikes trigger computation. No activity = No energy consumption.
Math Multiplication (A × B)
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(A × B): Power-hungry floating-point ops require complex ALU units in chips.
 Accumulate (A + B)
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(A + B): Simple binary addition. Reduces silicon footprint and heat generation by 90%.
Neuron Continuous Floats
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Information is a high-precision decimal (e.g. 0.823). Requires constant memory traffic.
 Discrete Spikes
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Information is a binary pulse (0 or 1). Mimics biological neuron firing.
Activity Always On
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The Sleep Factor: Looking at a blank wall costs the same energy as a crowd. Multipliers never rest.
 Event-Driven
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The Sleep Factor: Zero change = Zero spikes. The chip sleeps when idle, waking only for signal.
Energy 10 Joules
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Metric representing high-performance GPU energy consumption for a single inference.
 0.1 Joules
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Dramatic 100x efficiency gain by eliminating idle cycles and complex math.
Speed Frame-Based
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Waiting for the next clock cycle or video frame (e.g. 60Hz) creates bottleneck.
 Spike-Based
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Asynchronous processing. Information travels at the speed of the spike pulse.
Hardware GPU Clusters
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Requires expensive cloud infrastructure or data-center cooling systems.
 Neuromorphic
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Native fit for chips like Intel Loihi or BrainChip Akida. Runs on ultra-low voltage.

The Aneuro Paradigm

We've replaced matrix multiplications with sparse, binary spike computations. Our 0.8B model outperforms models 10x its size on industry benchmarks like MMLU-Pro Massive Multitask Language Understanding Pro: A rigorous benchmark evaluating reasoning and knowledge across 14,000+ tasks. .

Spiking Vision
Vision Breakthrough

Reflexive Vision

Traditional vision models process frames. Aneuro processes Events.

By moving to Event-Based Spiking ViTs Spiking Vision Transformers: A bio-inspired version of the Transformer architecture that uses temporal spikes instead of dense pixel frames. , we achieve microsecond latency - enabling reflex motor control for drones, robots, and autonomous vehicles.

Intelligence in the Wild

Robotics

Latency-Free Reflexes: Using MLSA Multi-Head Latent Spiking Attention: Our proprietary attention mechanism that combines spiking data with latent compression for zero-latency processing. , we eliminate frame-scan waits, enabling microsecond response times for complex motor control.

Autonomous

Event-Driven Dynamics: Pure additive math (Aneuro MLA Multi-Head Latent Attention: Our advanced compression technique that optimizes attention metadata to save memory without losing context. + SNN Spiking Neural Networks: The third generation of neural networks, designed to mimic biological neuron activity via discrete spikes. ) enables active on-demand efficiency, delivering massive throughput without the thermal/power spike of dense ViTs Vision Transformers: A deep learning model that applies the transformer architecture—originally designed for text—to process visual data in chunks or 'patches'. .

Defense

Threat-Active Edge: Our Spiking MoE Spiking Mixture of Experts: A sparse architecture where only a subset of specific experts fire in response to signal, drastically reducing power draw. activates only when threats are detected, ensuring efficient local reasoning on ultra-low power.

Aerospace

Memory Efficiency: Latent compression reduces KV cache Key-Value Cache: The memory used to store past conversation context. Our compression reduces this memory footprint by 90%. by 90%, enabling full-scale foundation models on small-sat hardware.

Dual-Pathway Scale

B2B / Industry

Aneuro Core

Strategic licensing for Robotics, Defense, and Aerospace OEMs. Our spiking FOUNDATION models integrate directly into next-gen industrial silicon.


  • • IP Licensing & Royalty Models
  • • Custom Mission-Specific Tuning
  • • Hardware-Agnostic "Aneuro-OS"
B2C / Consumer

Aneuro Edge

Native integration in Premium Consumer Hardware. Bringing ultra-efficient, private "Local Minds" to phones, tablets, and home bots.


  • • On-Device LLM/VLM Ecosystem
  • • Zero-Latency Voice & Vision
  • • 100% On-Device Privacy

Join the Revolution

We are live with a functional API and Control Panel. We are seeking strategic partners and pre-seed capital to scale our multi-modal spiking ecosystem.



Contact: info@aneurologic.com