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The 2nd Generation Akida builds upon BrainChip's neuromorphic legacy, broadening the range of supported complex network models with enhancements in weight and activation precision up to 8 bits. This generation introduces additional energy efficiency, performance optimizations, and greater accuracy, catering to a broader set of intelligent applications. Notably, it supports advanced features like Temporal Event-Based Neural Networks (TENNs), Vision Transformers, and extensive use of skip connections, which elevate its capabilities within spatio-temporal and vision-based applications. Designed for a variety of industrial, automotive, healthcare, and smart city applications, the 2nd Generation Akida boasts on-chip learning which maintains data privacy by eliminating the need to send sensitive information to the cloud. This reduces latency and secures data, crucial for future autonomous and IoT applications. With its multipass processing capabilities, Akida addresses the challenge of limited hardware resources smartly, processing complex models efficiently on the edge. Offering a flexible and scalable IP platform, it is poised to enhance end-user experiences across various industries by enabling efficient real-time AI processing on compact devices. The introduction of long-range skip connections further supports intricate neural networks like ResNet and DenseNet, showcasing Akida's potential to drive deeper model efficiencies without excessive host CPU calculation dependence.
MetaTF is BrainChip's toolset designed to facilitate the development and deployment of neural networks on their Akida platform. It simplifies the process of leveraging BrainChip’s AI capabilities by allowing the conversion of existing TensorFlow models to the Akida platform. Using Python and associated tools like Jupyter notebooks, MetaTF offers a seamless environment for training and deploying AI models optimized for event-based computations. The framework consists of multiple Python packages: the Akida package which provides an interface to BrainChip's neuromorphic chip, and CNN2SNN to convert convolutional neural networks for event domain processing. MetaTF's core value lies in streamlining the creation of low-latency, low-power networks that are inherently suited for BrainChip's portfolio of AI processors. With a built-in model zoo and performance simulation features, MetaTF enables users to evaluate and optimize models efficiently. This toolset ensures smooth integration with existing AI workflows by removing the necessity to adopt new machine learning paradigms completely, making it a vital component in BrainChip’s AI enablement strategy.
Akida IP is BrainChip's pioneering neuromorphic processor, crafted to mimic the human brain's analytic capabilities by processing only essential sensor inputs. This localized processing greatly enhances efficiency and privacy, as it significantly reduces the need for cloud data transactions. The processor offers scalable architecture supporting up to 256 nodes interconnected via a mesh network with each node composed of four configurable Neural Network Layer Engines. This event-based technology cuts down operations drastically compared to traditional methods, promoting lower power consumption. With robust support for on-chip learning and incremental learning capabilities, Akida IP is apt for a diverse range of applications and environments. The neural network processor adapts to real-time data seamlessly, creating new avenues for personalized and private on-device AI experiences. The architecture of Akida IP allows it to run complete neural networks, managing various neural network functions in hardware, thus optimizing resource utilization and power efficiency. Integrating Akida IP into systems is straightforward with BrainChip's development ecosystem, facilitating easy evaluation, design, and deployment processes. The Akida PCIe board and additional platform offerings, like the Raspberry Pi kit, promote seamless development and integration for intelligent AI endpoints, perfectly aligning with BrainChip's mission to streamline the implementation of edge AI solutions.
The Akida1000 Reference SoC represents BrainChip’s effort to provide a complete, event domain neural processing solution. This standalone device features comprehensive AI functionalities, supporting a vast network of 1.2 million neurons and 10 billion synapses. It is versatile in use, functioning independently or as a supportive co-processor across various applications, significantly improving edge AI deployment. BrainChip has integrated this SoC into reference development systems like Akida PCIe and Raspberry Pi, enabling working prototypes and AI system evaluations. Predominantly focused on efficient event-based computing, Akida1000 facilitates few-shot learning on-chip, which is critical for devices needing rapid personalization and adaptation without cloud reliance. Configuration flexibility allows seamless integration into diverse system architectures, backed by MetaTF tools that optimize design and deployment processes. Akida1000’s design ensures that neural processing remains power-efficient while maintaining high performance, critical for upcoming AI-driven smart device landscapes in consumer and IoT domains.
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