How It Works: Inceptron – Automated AI Compute Optimization

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How It Works

Inceptron – Automated AI Compute Optimization

This figure illustrates the end‑to‑end flow your team will follow to turn a raw machine‑learning model into a deployment‑ready, production‑grade runtime.

1. Bring your inputs

Model artefact – any Hugging Face, ONNX, Pytorch, or TensorFlow model, including fully‑custom architectures.

Sample data sets – a handful of representative inputs so the compiler can verify functional correctness during optimization.

2. Define your targets

Hardware – CPU (x86, ARM), GPU model (Nvidia, AMD).

Performance goal – latency, throughput, cost-optimized.

3. Pick an optimization track

Track

When to choose it

What we do

Accuracy‑Preserving

You want the exact same model outputs— No quantization allowed.

We apply lossless graph‑level transformations (e.g. Deepshift, Denseshift, mixed precision) plus memory & cache optimisation so you get speed‑ups without accuracy drift.

Use‑Case‑Tuned

You have a specific workload or KPI—e.g. maximise tokens/second for Llama for an ensemble of benchmarks of your choice

The compiler ingests your benchmark suite and desired metrics, then explores a larger search‑space (Bayesian optimisation, sparsity & quantisation passes) to co‑design the model and runtime for the best end‑to‑end baseline score that you need.

4. Automated passes

Compression – shift‑based re‑parameterisation, mixed precision floating‑point, and other lossless size reductions.
Memory optimisation – cache layout, weight packing, smart allocation to keep hot data on‑chip.
Model‑level automation – bitwise rewrites, sparsity encoding, affine/quantile quantisation, and data‑lookup acceleration.

5. Runtime synthesis

The compiler emits target‑specific kernels and, where relevant, partitions the graph across compute nodes for distributed execution.

6. Drop‑in output

A self‑contained Docker image that embeds the optimised model and runtime, ready to docker run in dev, staging, or prod.

In short: you give us a model and (optionally) a benchmark + KPI; we hand back a lean, ultra‑fast, accuracy‑guaranteed—or KPI‑optimised—runtime you can deploy anywhere.