
Key Benefits
Scalable Design to Adapt to Various AI Workloads
Efficient in Mapping State-of-the-Art AI/ML Workloads
Best-in-class performance for inferences per second with low latency and high throughput, with architectures optimized for achieving high performance within a low-energy profile
Industry-Leading Performance and Power Efficiency
High Inferences per second per area (IPS/mm2) and per power (IPS/W)
End-to-End Software Toolchain for All Markets and a Large Number of Frameworks
NeuroWeave SDK provides a common tool for compiling networks across IP, with flexibility for performance, accuracy, and run-time environments
Target Markets
Answering the Needs of a Wide Range of End Applications
Resources
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Offerings
Flexible and Configurable to Support Product Differentiation
NeuroWeave SDK
The NeuroWeave SDK is central to the Cadence AI IP portfolio. This powerful SDK offers a suite of tools, libraries, and drivers that efficiently map ML networks to all Cadence AI IP offerings and deploy them when ready. Users can develop AI networks using popular frameworks such as PyTorch, TensorFlow, TensorFlow Light Micro (TFLM), ONNX, and more. The NeuroWeave SDK quantizes and maps these networks to the targeted Cadence AI IP, allowing users to choose the optimal IP for performance, power, and area, all within a single, familiar SDK. When requirements change, the NeuroWeave SDK provides a straightforward path to explore other AI IP configurations, ensuring adaptability and ease of use. Unlock the full potential of AI integration with the NeuroWeave SDK.
Protocol IP for AI
Cadence Protocol IP for AI, including PCI Express® (PCIe®), UALink, CXL, Universal Chiplet Interconnect Express (UCIe™), and advanced memory interfaces such as HBM and GDDR, is engineered to optimize AI applications across markets. These technologies provide high-performance, low-latency interconnects for efficient data transfer and seamless communication, supporting various data speeds and configurations. Our AI systems handle intensive computational tasks and large datasets with enhanced performance, power efficiency, and scalability.
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