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 TensorFlow, Pytorch, or ONNX 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
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.