“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:
Benchmark Data:
Arithmetic format
Report Table 1 · single precision · relative error 0.00000
74.81mPJ
adaptive energy
55.89%
energy savings
99.68%
precision
97.80%
recall
0.21%
false wake rate
Dense baseline vs adaptive router
Table 1 · all formats
| Format | Baseline | Adaptive | Savings | Prec. | Recall | FWR | Rel. err. |
|---|
Benchmark Data:
Report Table 2 · the five noise levels the report measured
1.024
threshold θ
0.915
decay rate β
0.21%
false wake rate
99.68%
precision
97.80%
recall
Figure 1a · Adaptive parameters
Figure 1b · Detection quality
Between nominal conditions and 1.50σ, the feedback loop raises θ from 1.024 to 1.490 and lowers β from 0.915 to 0.720, holding the false wake rate below 1.65%. The cost is real and the report states it plainly: precision falls from 99.68% to 94.88% and recall from 97.80% to 91.45%. The router stays stable, but detection quality does degrade as noise rises.
Values from report Table 2 / Figure 1. The noise model changes from Gaussian to Poisson / combined at 1.20σ. Hover a chart to read a point; click to select it.
Benchmark Data:
Technical benchmark & evaluation report
Every figure on this page comes from this report: the energy comparison, the FP32 / FP16 / INT8 results and the noise sweep up to 1.50σ. Download it to read the method, tables and references in full.
Method at a glance
Each leaky integrate-and-fire unit updates its membrane potential, and the threshold adapts to keep the firing density on target.
Reproducibility
Section 5 of the report states that its release bundle contains the complete numerical logs (snn_power_router_log.json) and the execution scripts (snn_power_router.py), provided so every reported metric can be verified.
The report also lists the prior work it builds on: