AI Engineering — ZERLON SEMI

AI Engineering

Custom NPU design and ML hardware architecture — accelerators co-designed with the models they run, tuned for maximum TOPS/W from the data center to the edge.

Design an Accelerator Co-Design Flow

NPU DesignSystolic ArraysQuantizationEdge InferenceNeural IP

TOPS/W

Efficiency-First

INT8 ·
BF16

Mixed Precision

Edge →
Data Center

Full Spectrum

Model +
Silicon

Co-Designed

Capabilities

Hardware built for the models it runs.

We architect accelerators from the workload down — so every joule and every square millimetre earns its keep.

NPU Microarchitecture

Systolic arrays, vector engines, and dataflow accelerators sized precisely to your model's compute and memory profile.

Dataflow & Memory

Tiling, operand reuse, and on-chip memory hierarchies that keep the PEs fed and off-chip bandwidth low.

Quantization & Sparsity

INT8/BF16 mixed precision, structured sparsity, and pruning-aware hardware for more throughput per watt.

Edge Inference

Ultra-low-power accelerators for always-on, battery, and thermally-constrained edge devices.

Neural IP

Reusable, silicon-proven accelerator IP blocks that integrate cleanly into your SoC.

Compiler & Toolchain

Model-to-hardware compilation, graph optimization, and runtime so your networks map efficiently to silicon.

Model–Silicon Co-Design

From workload to watt-optimal silicon.

Great AI hardware isn't designed in isolation — it's shaped by the model. We profile your networks first, then architect the accelerator around them.

Profile the model — operators, data types, sparsity.
Map the dataflow — tiling, reuse, memory hierarchy.
Architect the NPU and compile the toolchain.
Optimize for the highest achievable TOPS/W.

Where It Runs

Acceleration, everywhere.

Data Center

Training and high-throughput inference.

Mobile & Edge

On-device vision, speech, and LLMs.

Automotive AI

Perception and autonomy compute.

IoT & Sensors

Always-on tinyML at microwatts.

FAQ

Questions, answered.

Yes — that's the point of co-design. We profile your networks (operators, precision, sparsity) and architect the NPU, memory hierarchy, and dataflow around them for the best efficiency.

INT8 and BF16 mixed precision, structured sparsity, pruning-aware datapaths, and quantization flows — traded off against your accuracy and power targets.

Yes. Hardware without a toolchain is hard to use — we provide model-to-hardware compilation, graph optimization, and runtime so your models map efficiently to the silicon.

Absolutely. We provide reusable neural IP blocks designed to integrate cleanly into your existing SoC and toolflow.

Let's Build

AI silicon, designed around your model.

NPUs and neural IP tuned for maximum performance per watt — edge to data center.

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