Why NetsPresso is needed
Without NetsPresso
As AI models continue to evolve—from large vision transformers to lightweight models for edge deployment—ensuring smooth compatibility with diverse hardware environments becomes increasingly complex. Each hardware platform has its own architecture and supported operator set, which can make seamless AI deployment a significant challenge.
Many AI models today rely on a wide range of advanced and specialized operators. Depending on the hardware, some of these may be unsupported, potentially leading to fallback to CPUs and a resulting drop in inference performance and efficiency.
To address this, developers often need to manually adapt their models through operator conversion, graph simplification, and quantization—tasks that require deep hardware understanding and involve substantial trial and error.
| Challenge | Description |
|---|---|
| ❗ Compatibility issues between model and device | SDKs may not support required operators or data types, causing model compilation failures |
| ❗ Inconsistent optimization quality | Manual pruning/quantization may lead to unstable results, lower accuracy, and reduced performance |
| ❗ Tedious model framework migration | Developers must manually convert or re-implement models to move between TensorFlow, PyTorch, or ONNX |
| ❗ Monolithic, rigid SDK integration | SDKs often require full toolchain adoption, even if only part of the functionality is needed |
| ❗ Lack of real-device testing | No simulation or real-device validation; performance discrepancies between dev and deployment stages |
| ❗ Limited access interfaces | CLI-only tools limit non-technical users and reduce workflow efficiency |
With NetsPresso
NetsPresso simplifies and automates this entire optimization process, helping developers efficiently deploy AI models across a variety of hardware platforms—without needing to replace hardware or manually re-engineer models.
| Advantage | Description |
|---|---|
| ✅ Ensures model–device compatibility | Automatically resolves operator mismatches and supports quantization to match target device constraints |
| ✅ Enhances optimization quality | Enables advanced compression and quantization while maintaining accuracy and improving speed |
| ✅ Enables flexible model framework support | Allows seamless transformation of models across frameworks via an IR conversion layer |
| ✅ Provides efficient modular SDK integration | Each function is modularized so users can selectively use training, optimization, and testing stages |
| ✅ Supports real-device validation | Allows tests to run on 50+ edge devices before deployment, ensuring performance and compatibility |
| ✅ Offers both GUI and CLI access | GUI for intuitive visual flows and Python SDK for advanced automation and customization |
Updated 4 days ago