Origin Evolution for Automotive
AI Inference for the Most Advanced Automotive Needs
Whether deployed in-cabin for driver distraction or in the advanced driver assistance system (ADAS) stack for object recognition and point cloud processing, AI forms the backbone of the future of safer, smarter cars.

AI Demands Higher Performance
Auto makers are adding more AI, including advanced LLM and multimodal capabilities, as they enable safety and usability use cases such as autonomous driving/ADAS, driver attention monitoring, passenger detection, and infotainment. While local inference is essential for all safety-critical systems, LLMs may be 20 to 50X larger than more traditional AI networks. Automotive inference capabilities on today's processors are already limited, and automakers are looking for alternative, more efficient architectures for next generation systems.
Ideal Processing Architecture
Origin Evolution™ for Automotive offers out-of-the-box compatibility with popular LLM and CNN networks. Attention-based processing optimization and advanced memory management ensure optimal AI performance across a variety of today’s standard and emerging neural networks. Featuring a hardware and software co-designed architecture, Origin Evolution for Automotive scales to 96 TFLOPS in a single core, with multi-core performance to PetaFLOPs.
Innovative Architecture
Origin Evolution uses Expedera’s unique packet-based architecture to achieve unprecedented NPU efficiency. Packets, which are contiguous fragments of neural networks, are an ideal way to overcome the hurdle of large memory movements and differing network layer sizes, which are exacerbated by LLMs. Packets are routed through discrete processing blocks, including Feed Forward, Attention, and Vector, which accommodate the varying operations, data types, and precisions required when running different LLM and CNN networks. Origin Evolution includes a high-speed external memory streaming interface that is compatible with the latest DRAM and HBM standards.
Choose the Features You Need
Reducing Memory Bandwidth
Efficient Resource Utilization
Full Software Stack
Origin Evolution offers out-of-the-box support for 100+ popular neural networks, including Llama2, Llama3, ChatGLM, DeepSeek, Mistral, Qwen, MiniCPM, Yolo, MobileNet, and many others.
Unique Packet Architecture
Ultra-Efficient Neural Network Processing
Accepting standard, custom, and black box networks in a variety of AI representations, Origin Evolution offers a wealth of user features such as mixed precision quantization. Expedera’s unique packet-based processing reduces much larger networks into smaller, contiguous fragments, overcoming the hurdle of large memory movements and offering much higher processor utilization. Packets are routed through discrete processing blocks, including Feed Forward, Attention, and Vector, which accommodate the varying operations, data types, and precisions required when running different types of networks. Internal memory handles intermediate needs, while the memory streaming interface interfaces with off-chip storage.
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Compute Capacity | up to 48 FP16 MACs |
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Multi-tasking | Run Simultaneous Jobs |
Example Networks Supported | Llama2, Llama3, ChatGLM, DeepSeek, Mistral, Qwen, MiniCPM, Yolo, MobileNet, and many others, including proprietary/black box networks |
Example Performance | 261 tokens per second, DeepSeek v3 token generation, 64 TFLOPS engine, batch size of 512, 256 GB/s external peak bandwith, 4.391W peak power consumption. Specified in TSMC 7nm, 1 GHz system clock, no sparsity/compression/pruning applied (though supported) |
Layer Support | Standard NN functions, including Transformers, Conv, Deconv, FC, Activations, Reshape, Concat, Elementwise, Pooling, Softmax, others. Support for custom operators. |
Data types | FP16/FP32/INT4/INT8/INT10/INT12/INT16 Activations/Weights |
Quantization | Software toolchain supports Expedera, customer-supplied, or third-party quantization. Mixed precision supported. |
Latency | Deterministic performance guarantees, no back pressure |
Frameworks | Hugging Face, Llama.cpp, PyTorch, TVM, ONNX. Tensor Flow and others supported |
Safety | ASIL-B readiness, ISO 9001:2015 |