“Scaling intelligence, not energy consumption.”
Cogitar is an advanced technology initiative pioneering neuromorphic compute architectures and energy-efficient large language model inference. Born out of an uncompromising standard for independent engineering, the company focuses on spiking neural network gating and intelligent power routing to dramatically reduce the energy footprint of artificial intelligence.
We engineer quantum-inspired algorithms and surrogate-gradient architectures designed to route power dynamically across computational networks. By replacing continuous energy draw with event-driven precision, we drastically cut inference overhead without sacrificing intelligence or performance.
Current Projects:
Dynamic Leaky Integrate-and-Fire (LIF) spiking neural network units that gate execution systems based on event-driven sensory input rather than continuous clock cycles.
Delivers a 55.89% energy reduction in FP32 operation (dropping from 169.58 mPJ down to 74.81 mPJ) while maintaining 99.68% precision and a 0.21% false wake rate.
Current Projects:
Maintains robust savings (55.59% energy reduction) and stability down to INT8 quantized integers with a minimal relative error of 0.01386.
Utilizes real-time feedback loops to dynamically tune firing thresholds (θ) and membrane decay rates (β), keeping the false wake rate under 1.65% even under extreme stochastic or Poisson/
Current Projects:
High-dimensional anomaly detection, manifold alignment, and data validation built for high-throughput engineering pipelines.
Fully deployed and maintained as an open-source library on both PyPI and npm, bringing deterministic data integrity checking directly to Python and JavaScript workflows.
Current Projects:
Designed to catch data drift, flag structural anomalies, and verify edge-case manifolds before they corrupt model training or compromise inference streams.
Fully deployed and maintained as an open-source library on both PyPI and npm, bringing deterministic data integrity checking directly to Python and JavaScript workflows.
Current Projects: