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The SAKURA-II AI Accelerator by EdgeCortix is an advanced processor designed for energy-efficient, real-time AI inferencing. It supports complex generative AI models such as Llama 2 and Stable Diffusion with an impressive power envelope of just 8 watts, making it ideal for applications requiring swift, on-the-fly Batch=1 AI processing. While maintaining critical performance metrics, it can simultaneously run multiple deep neural network models, facilitated by its unique DNA core. The SAKURA-II stands out with its high utilization of AI compute resources, robust memory bandwidth, and sizable DRAM capacity options of up to 32GB, all in a compact form factor. With market-leading energy efficiency, the SAKURA-II supports diverse edge AI applications, from vision and language to audio, thanks to hardware-accelerated arbitrary activation functions and advanced power management features. Designed for ARM and other platforms, the SAKURA-II can be easily integrated into existing systems for deploying AI models and leveraging low power for demanding workloads. EdgeCortix's AI Accelerator excels with innovative features like sparse computing to optimize DRAM bandwidth and real-time data streaming for Batch=1 operations, ensuring fast and efficient AI computations. It offers unmatched adaptability in power management, enabling ultra-high efficiency modes for processing complex AI tasks while maintaining high precision and low latency operations.
The Dynamic Neural Accelerator II (DNA-II) by EdgeCortix is a versatile and powerful neural network IP core tailored for edge AI applications. Featuring run-time reconfigurable interconnects, it achieves high parallelism and efficiency essential for convolutional and transformer networks. DNA-II can be integrated with a variety of host processors, rendering it adaptable for a wide range of edge-based solutions that demand efficient processing capabilities at the core of AI advancements. This architecture allows real-time reconfiguration of data paths between DNA engines, optimizing parallelism while reducing on-chip memory bandwidth via a patented reconfigurable datapath. The architecture significantly enhances utilization rates and ensures fast processing through model parallelism, making it suitable for mission-critical tasks where low power consumption is paramount. DNA-II serves as the technological backbone of the SAKURA-II AI Accelerator, enabling it to execute generative AI models proficiently. This innovative IP core is engineered to mesh effortlessly with the MERA software stack, optimizing neural network operations through effective scheduling and resource distribution, representing a paradigm shift in how neural network tasks are managed and executed in real-time.
EdgeCortix’s MERA Compiler and Software Framework serves as a comprehensive platform for deploying neural network models across various systems. It facilitates AI inference by combining the Dynamic Neural Accelerator (DNA) technology, offering tools like APIs, a code generator, and runtime capabilities essential for implementing pre-trained deep neural networks. The framework is engineered to tackle innovative generative AI applications, accommodating a spectrum of leading processors, such as AMD, Intel, Arm, and RISC-V, ensuring speedy integration into existing infrastructures. MERA stands out by supporting an array of ML frameworks, including TensorFlow Lite and ONNX, and offering native interfaces for Python and C++ to customize AI workflows. It simplifies the development and deployment process with post-training model calibration and quantization. The inclusion of Apache TVM and MLIR adds flexibility, allowing developers to advance from modeling to deployment with ease. The framework is integral to the company's suite of offerings as it optimizes neural network computation, resource allocation, and execution order, all within a runtime reconfigurable architecture. The MERA platform thus grants software developers the ability to focus on AI model refinement without getting entangled with hardware intricacies, dramatically reducing latency and boosting throughput efficiency.
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