Power - Graph + Core Sophisticated PSU & Power Grid design elements with Power (Watts & Volts) / Thermal & Control

Power Control the PSU, A special Graph (c)RS

Core Sophisticated PSU & Power Grid design elements with Power (Watts & Volts) / Thermal & Control:

Power Control & PSU, A special Graph message illustrating an advancement on the the already revealed NPU & MCU Power-Control Firmware By RS

Now as you know, I wrote about Power Control for PSU & Power Grids,

Now Graphs, We know clever PSU illustrate the power levels on the PC appliance app & yes this is clever! :p & Expensive, Right?! Very Expensive..

Now i have an AX1000 WATT PSU by corsair, Refurbished & good, Apart from the fan..

Graphs traditionally show a flat graph in 2D, To the same level as AMD GPU Driver thermal panel,

These graphs are useful & simple enough to understand, 3D graphs are fly & all that, But they hardly improve on the knowledge you receive..

We could ofcourse use 3D Graphs, 2D & 3D Jacobian Graphs, SVM & Statistical maths..

The PSU & the GPU,.. Processors & motherboard style VThermal Illustrative graphs..

The power control itself is what we are after automating, So we are going to create a short list for improving the internal control..

Internal control MCU Control with SVM

With SVM & Elliptic Curve Maths Emulated SVM (Graph Fitting), We can optimise the graphs against benchmark database, MicroDB..

So PSU & GPU don't have internal storage for dynamic databases, What do we do?

Database RAM Cache, Yes even 15KB!, Databases can be small, 8KB..

ENV : Arrays, We could use environment arrays stored in OS RAM? Basically an IndexDB

We want to control the Fan & the Vthermal & VRM? What to do?

SVM & Jacobian Graph, Fitting, This means ML, Programming or data analytics engineering.

(c)Rupert Summerskill

Example IndexDB Set:

// Reference Dataset for model with LightML + SVM + ECC 3D Manifold Regression

// Standard of Drift in NTP & PTP

Network + NTP + PTP + gPTP Time Server

+ Resolver Data + Data Plane

(
LightML + SVM + ECC 3D Manifold Regression = (

( Local Drift File Data );
( Global Drift File Data );
( GNSS Relativistic Corrections );
( Gravity Model );
( Fibre Dispersion Model );
( Network Bandwidth Model );
( Network Latency Model );
( Network Protocol Model );
( Network Routing Model );
( Master Security Model );
( Network Security Model );
( Application Security Model );

),

LightML + SVM Resolve → Consensus Time Table

);

//(c)RS

We need power control in it, That's it!

// Reference Dataset for Hardware Power & Thermal Control
// Applying LightML + SVM + ECC 3D Manifold Regression for PSU/GPU Automation
// (c) Rupert Summerskill

// Standard of VThermal, VRM Stability & Acoustic Output

Power Control + PSU + GPU + VRM Automation

+ Sensor Resolver Data + Power Plane

(

LightML + SVM + ECC 3D Manifold Regression = (

( Local Transient Load Profile ); // Real-time current (Amps) spikes
( MicroDB Cache State ); // The 8-15KB Dynamic RAM Cache from OS
( Vthermal Resistance Model ); // Heat saturation over time (Heatsink capacity)
( VRM Efficiency Curve ); // Voltage regulation mapping (Sweet spots)
( Fan Acoustic/Thermal Jacobian ); // 3D Matrix: rate of temperature change vs. RPM
( Component ACPI Power State ); // Sleep/Wake/Boost signals from the motherboard
( Input Voltage Ripple Model ); // PSU AC-side grid stability & capacitance
( Predictive Power State Drift ); // Expected load based on historical SVM vectors

),

LightML + SVM Resolve → Consensus Power & Thermal State

);

//(c)RS

How the Math Works in the Firmware

By structuring the firmware this way, you are fundamentally changing how the hardware operates:

The Jacobian Graph Fitting: Instead of a flat 2D fan curve (e.g., "If 60°C, then 50% fan speed"),

the Jacobian matrix calculates the rate of change. If the GPU spikes by 10°C in one second,

The firmware knows a massive load just hit and ramps the VRM and fan up before the thermal limit is breached.

SVM and ECC on 15KB: Support Vector Machines are perfect for this because once the model is trained, ..

The resulting vectors (the boundaries of what is considered "optimal power") take up virtually no memory..

You don't need to store a massive database of past temperatures on the PSU,..

You just store the boundary equations in that 8KB–15KB cache.

The IndexDB Bridge: The heavy lifting, storing the historical benchmarks and large datasets.. Lives in the OS RAM..

The software feeds only the highly compressed, refined LightML weights down to the PSU/GPU MCU via USB or PCIe headers.

This gives you a system that constantly adjusts its own efficiency curve, maximizing power delivery while keeping acoustic noise to an absolute minimum.

(c)RS

*****

Core Sophisticated PSU & Power Grid design elements with Power (Watts & Volts) / Thermal & Control: Design Elements...

1. Conceptual architecture

Layers:

OS Layer (Heavy ML + IndexDB):

Role: Train, refine, and compress models; maintain historical datasets.

Storage: OS RAM + disk; IndexDB-style micro-DBs per device (PSU, GPU, VRM).

Output: Tiny LightML/SVM parameter sets + ECC manifold coefficients, pushed down as “profiles”.

MCU Layer (Reflex Engine, 8–15KB Cache):

Role: Real-time control loop for fan, VRM, and power state.

Storage:

Boundary equations: SVM hyperplanes, Jacobian coefficients, ECC manifold parameters.

MicroDB cache: A few recent state vectors + profile metadata (version, checksum, validity window).

Sensor/Power Plane:

Inputs: Temperature (GPU, VRM, PSU), current draw, voltage ripple, ACPI power states, transient load spikes.

Outputs: Fan RPM, VRM voltage/current limits, PSU rail behaviour, “soft” power caps.

2. The IndexDB / MicroDB model

OS + Firmware (USB Stick or Flash Card Port, ideal for big data), IndexDB:

Tables (conceptual):

thermal_events:
Fields: timestamp, device, temp_before, temp_after, load_vector, fan_rpm, VRM_state.

power_transients:
Fields: amps_spike, duration, rail, ripple, resulting temp delta.

profiles:
Fields: profile_id, device_type, SVM_params, ECC_params, Jacobian_matrix, validity_range.

Job:

Aggregate events → train LightML/SVM + ECC manifold.

Compress to boundary equations + Jacobian matrices.

Emit profile blobs sized to fit 8–15KB MCU cache.

MCU-side MicroDB cache (8–15KB):

Layout (example):

Header (64–128B): profile_id, version, CRC, timestamp, device mask.

SVM block (~2–4KB): support vectors + coefficients (heavily quantized, e.g. INT8/INT4).

Jacobian block (~2–4KB):

Fan acoustic/thermal Jacobian

VThermal resistance coefficients

VRM efficiency curve segments.

ECC manifold block (~2–4KB): compact curve parameters for non-linear regions.

Scratch state (~1–2KB): last N state vectors (e.g. 8–16 samples) for drift estimation.

3. Control loop in firmware

3.1 Input vector

Every control tick (e.g. every 10–50ms), MCU builds:

State vector x:

T_gpu – current GPU temp

T_vrm – VRM temp

T_psu – PSU internal temp

I_load – instantaneous current draw

dT_gpu/dt – temp rate of change

ACPI_state – S0/S3/S5, boost flags

V_ripple – input voltage ripple

profile_id – active profile selector (optional bitmask)

3.2 Jacobian fan/VRM response

Instead of a static curve:

Jacobian matrix J approximates:

Δ𝑢=𝐽⋅Δ𝑥

where:

Δ𝑥 = change in state (e.g. +10°C in 1s, +20A spike)

Δ𝑢 = change in control outputs (fan RPM, VRM margin, soft power cap).

Example behaviour:

If 𝑑𝑇𝑔𝑝𝑢/𝑑𝑡 is high, even if absolute temp is “safe”, firmware pre-emptively:

Boosts fan RPM aggressively for a short window.

Tightens VRM limits to avoid overshoot.

This is the “anticipatory” part we describe, Reacting to rate not just level.

3.3 SVM boundary check

SVM model: defines “optimal region” in state space:

Region A: Silent/eco

Region B: Balanced

Region C: Performance/boost

Region D: Protection/derate

MCU evaluates:

𝑦=sign(∑𝑖𝛼𝑖𝐾(𝑥,𝑥𝑖)+𝑏)

with a very small set of support vectors ..

𝑥𝑖, quantized coefficients 𝛼𝑖, and simple kernel (e.g. linear or low-order polynomial).

Result:

Selects which policy to apply to Jacobian outputs:

In Silent region: cap fan RPM, allow slightly higher temps.

In Performance region: allow higher VRM output, more aggressive fan.

In Protection region: hard limits, ramp fan, possibly signal OS to throttle.

3.4 ECC manifold for non-linear zones

ECC manifold: used where behaviour is strongly non-linear:

Near thermal saturation of heatsinks.

At VRM efficiency “knees”.

Under unstable input ripple.

MCU uses small ECC curve parameters to warp the Jacobian/SVM outputs in those zones, e.g.:

Curve: maps “requested fan RPM” to “actual effective cooling” based on VThermal saturation.

Prevents overconfidence in fan ramps when heatsink is already saturated.

4. Power control integration

We sketch the secondary dataset; here’s how it plugs in:

Local Transient Load Profile:

Short window buffer (e.g. last 1–2s of I_load, T_gpu, T_vrm).

Used to compute

𝑑𝑇𝑑𝑡, spike detection, and feed Jacobian.

MicroDB Cache State:

Holds current profile + a few recent state vectors.

Enables Predictive Power State Drift: “we’ve seen this pattern → expect boost soon”.

Component ACPI Power State:

If OS signals upcoming boost (e.g. game launch, render job), MCU can pre-warm VRM and fan.

Input Voltage Ripple Model:

If grid/AC-side is unstable, firmware can slightly lower the (Volt to WATT) vs VRM rate power or..

Adjust VRM behaviour to protect components, Aka..

Core Sophisticated PSU & Power Grid design elements:

Thermal capping, Power levelling, Surge Protection..

Capacitor Buffering spikes & auto de-levelling power fluctuations in dynamic power versus VThermal, Requirements & Graphed Optimums.. 

5. Data path: OS ↔ PSU/GPU

Transport options:

USB HID / vendor-specific: for PSU.

PCIe sideband / SMBus / I2C: for GPU/VRM.

Protocol idea:

Profile Update Frame:

Header: device_id, profile_id, version, size, CRC.

Payload: SVM block, Jacobian block, ECC block.

Flags: “safe to apply live” vs “apply on next idle”.

(c)RS

*****

NTP + PTP + Networking & JIT:

On the topic of JIT, Sharing & Timer values, PTP & NTP ( Audio, Video, Gaming, Science )

https://science.n-helix.com/2026/08/power.html

https://science.n-helix.com/2022/01/ntp.html

https://science.n-helix.com/2023/06/ptp.html

https://science.n-helix.com/2022/08/jit-dongle.html

https://science.n-helix.com/2022/06/jit-compiler.html