Chimera GPNPU delivers full Vision Transformer support—including attention mechanisms, LayerNorm, and GELU—without the operator limitations of legacy NPUs.
The Challenge
Vision Transformers have revolutionized computer vision, outperforming CNNs on classification, detection, and segmentation. But their architecture is fundamentally different from what legacy NPUs were designed to accelerate.
| Capability | Legacy NPUs (2018-2020) | Chimera GPNPU |
|---|---|---|
| Optimized for | Conv2D and pooling | Any ML operator |
| Dataflow | Fixed for ResNet-style networks | Flexible for any topology |
| Attention support | Limited or none | Native multi-head attention |
| Activations | Hardcoded functions | Full GELU, LayerNorm support |
| Unsupported ops | CPU fallback required | Complete graph execution in Chimera |
Multi-head self-attention requires matrix multiplications that don't map efficiently to legacy NPU systolic arrays.
LayerNorm, GELU, and Softmax often fall back to CPU, destroying performance and power efficiency.
Variable sequence lengths in transformers break fixed-tensor NPU compilers designed for static workloads.
Attention's O(n²) memory access pattern overwhelms legacy NPU memory hierarchies built for sequential access.
The Solution
Chimera's General-Purpose NPU architecture was designed from the ground up to handle any ML workload—including the unique demands of transformer models.
Every ViT operator runs natively on Chimera—no CPU fallback, no operator gaps. Multi-head attention, LayerNorm, GELU, Softmax, and patch embedding all execute on the GPNPU with full hardware acceleration.
Our Chimera Graph Compiler (CGC) automatically recognizes and optimizes transformer attention patterns, mapping them efficiently to Chimera's compute array and memory hierarchy.
Chimera's software-managed memory handles the variable access patterns of attention without the rigidity of hardware-managed caches. Configure OCM size to match your model's requirements.
Model Support
Run industry-standard Vision Transformers on Chimera today, with more models coming soon. All available models are ready for immediate evaluation in DevStudio.
Hierarchical vision transformer with shifted windows
Bird's-eye-view transformer for 3D perception
Bring your own Vision Transformer architecture
New models regularly: CGC compiler supports standard transformer architectures. Port from ONNX without RTL changes.
Technical Capabilities
Every capability Vision Transformers require, accelerated in hardware.
Native hardware support for scaled dot-product attention with configurable head counts and embedding dimensions.
Hardware-accelerated Layer Normalization without CPU fallback. Handles pre-norm and post-norm architectures.
Full support for GELU, Softmax, and all standard activation functions used in transformer architectures.
Efficient image-to-patch conversion with configurable patch sizes and embedding dimensions.
INT8 symmetric and asymmetric quantization with Quantization-Aware Training (QAT) flow for accuracy preservation.
Scale ViT inference across multiple Chimera cores for higher throughput with data parallelism.
View cycle-accurate performance metrics, explore the compiled graph, and evaluate Chimera for your application.
Our engineering team can help you evaluate custom architectures, optimize performance, and plan your deployment on Chimera GPNPU.
Contact Our Team
