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Akida Neural Processor IP is a groundbreaking component offering a self-contained AI processing solution capable of locally executing AI/ML workloads without reliance on external systems. This IP's configurability allows it to be tailored to various applications, emphasizing space-efficient and power-conscious designs. Supporting both convolutional and fully-connected layers, along with multiple quantization formats, it addresses the data movement challenge inherent in AI, significantly curtailing power usage while maintaining high throughput rates. Akida is designed for deployment scalability, supporting as little as two nodes up to extensive networks where complex models can thrive.
The second generation of BrainChip's Akida platform expands upon its predecessor with enhanced features for even greater performance, efficiency, and accuracy in AI applications. This platform leverages advanced 8-bit quantization and advanced neural network support, including temporal event-based neural nets and vision transformers. These advancements allow for significant reductions in model size and computational requirements, making the Akida 2nd Generation a formidable component for edge AI solutions. The platform effectively supports complex neural models necessary for a wide range of applications, from advanced vision tasks to real-time data processing, all while minimizing cloud interaction to protect data privacy.
MetaTF is BrainChip's machine learning framework for developing systems on the Akida neural processor. Designed to aid in creating, training, and testing neural networks, MetaTF integrates seamlessly with TensorFlow models. Its key feature is the ability to convert CNN models to Spiking Neural Networks (SNN), facilitating low-latency, low-power operations suited for edge environments. By utilizing Python scripting and tools, MetaTF simplifies model conversion and optimization, delivering automatic CNN to SNN transitions without needing to learn new frameworks. MetaTF also includes various development tools, encompassing runtime simulation and robust testing environments.
BrainChip's Akida IP is an innovative neuromorphic processor that emulates the human brain's functionalities to analyze essential sensor inputs at the acquisition point. By maintaining AI/ML processes on-chip, Akida IP minimizes cloud dependency, reducing latency and enhancing data privacy. The scalable architecture supports up to 256 nodes interconnected over a mesh network, each node equipped with configurable Neural Network Layer Engines (NPEs). This event-based processor leverages data sparsity to decrease operational requirements significantly, which in turn improves performance and energy efficiency. With robust customization and the ability to perform on-chip learning, Akida IP adeptly supports a wide range of edge AI applications while maintaining a small silicon footprint.
The Akida1000 Reference SoC is a complete neural processing device designed for integration into AI systems, featuring 1.2 million neurons and 10 billion synapses. This SoC is configurable as a standalone embedded AI accelerator or can be paired with an MCU or CPU for enhanced performance. The Akida1000 uses event-based processing optimized for real-time neural network tasks, gaining efficiency from its large SRAM and scalable neural processing units, which are fully configurable to meet application-specific needs. It supports fully integrated networks, eliminating the need for frequent weight swaps between memory systems, thus enhancing throughput and reducing power consumption.
The Akida1000 is a sophisticated reference chip that showcases the full potential of BrainChip's neural processing capabilities. It is engineered specifically for low-power, high-efficiency AI computation both as a standalone solution or a co-processor to augment existing systems. Emphasizing event-based processing, this chip comprises numerous configurable neural processing engines that enable it to perform complex neural tasks directly on-device. Its architecture allows for seamless integration into various technologies while providing a comprehensive package including RTL, development tools, and documentation to facilitate its adoption and use in diverse application areas.
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