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The Dynamic Neural Accelerator II by EdgeCortix is a pioneering neural network core that combines flexibility and efficiency to support a broad array of edge AI applications. Engineered with run-time reconfigurable interconnects, it facilitates exceptional parallelism and efficient data handling. The architecture supports both convolutional and transformer neural networks, offering optimal performance across varied AI use cases. This architecture vastly improves upon traditional IP cores by dynamically reconfiguring data paths, which significantly enhances parallel task execution and reduces memory bandwidth usage. By adopting this approach, the DNA-II boosts its processing capability while minimizing energy consumption, making it highly effective for edge AI applications that require high output with minimal power input. Furthermore, the DNA-II's adaptability enables it to tackle inefficiencies often seen in batching tasks across other IP ecosystems. The architecture ensures that high utilization and low power consumption are maintained across operations, profoundly impacting sectors relying on edge AI for real-time data processing and decision-making.
The SAKURA-II AI Accelerator is a state-of-the-art energy-efficient AI processing unit designed to meet the demanding needs of generative AI applications. Boasting up to 60 TOPS performance, this accelerator is built on EdgeCortix's patented Dynamic Neural Accelerator architecture that allows high efficiency and low power consumption in edge AI tasks. It supports a wide array of models including Llama 2, Stable Diffusion, ViT, and large transformer and convolutional networks, typically operating within an 8W power envelope. Offering enhanced DRAM bandwidth and a maximum memory capacity of 32GB, SAKURA-II is optimized for real-time data streaming and efficient handling of large language models (LLMs) and vision-based AI workloads. This accelerator achieves exceptionally high AI compute utilization and provides robust energy-efficient performance, making it ideal for advanced applications in vision, language, audio, and more. The accelerator is available in modular M.2 and PCIe card formats, facilitating seamless integration into existing systems. These form factors ensure that the SAKURA-II can be effortlessly integrated into space-constrained or power-limited environments, providing the best choice for both development and deployment phases of edge AI initiatives.
The MERA Compiler and Framework is a pioneering software platform crafted by EdgeCortix to streamline AI inference deployment across different systems. This innovative framework facilitates comprehensive neural network model integration by offering tools, APIs, and runtime support necessary for implementing pre-trained deep neural networks. Its agnostic design supports popular frameworks such as PyTorch, TensorFlow Lite, and ONNX, enabling smooth interoperability across diverse development environments. By providing native compatibility with a host of processors including AMD, Intel, ARM, and RISC-V, MERA makes it easier for developers to integrate EdgeCortix's AI solutions into their existing setups. Furthermore, it leverages advanced functionalities such as Apache TVM and MLIR to aid in the seamless distribution of IP across development teams, enhancing both productivity and flexibility in edge AI application modeling. MERA's key capabilities include the ability to import pre-defined models from platforms like Hugging Face and the EdgeCortix Model Library, followed by post-training quantization and calibration. This ensures the deployment of models with near-native efficiency, particularly in demanding Generative AI tasks, thus reducing both time-to-market and operational complexities.
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