Logorythms

We're engineering intelligence at the intersection of physics, neuroscience, and computation. Our approach operates on three foundational principles:

Neuromorphic Hardware

Traditional chips process information sequentially, moving data between separate memory and compute units. Our neuromorphic systems embed memory directly into processing elements, mimicking how biological neurons store and compute simultaneously.

This eliminates the energy-intensive data shuffling that dominates conventional AI, reducing power consumption by orders of magnitude while enabling massively parallel computation.

Event-Driven Processing

Instead of continuous computation on a clock cycle, our systems process information only when meaningful events occur—like spike-based communication in neural networks.

This sparse, asynchronous approach dramatically reduces energy consumption while maintaining high computational throughput, particularly for real-world tasks with naturally sparse temporal structure.

Adaptive Materials

We leverage materials that can modify their properties based on history and context—memristors, phase-change materials, and 2D electronic structures that adapt their conductivity through use.

This enables hardware that learns and evolves, implementing plasticity mechanisms directly in silicon rather than simulating them through software.

System Architecture

Hierarchical Processing

Multi-scale processing layers from low-level sensory integration to high-level reasoning, each optimized for its computational demands and temporal dynamics.

Embodied Learning

Tight coupling between perception and action, enabling real-time adaptation to environmental changes through continuous sensorimotor feedback loops.

Distributed Memory

Information stored throughout the network rather than in centralized memory banks, enabling fault tolerance and parallel access patterns that scale naturally.

"When computation emerges from physics,
intelligence becomes as efficient as life itself."